0:55
Welcome!  My name is Kurt Cagle The managing editor for the data science central part of the tech target network. I talk the show be about a month ago about what was going on here. Specifically, looking at the role of ethics, specifically talking about AI deep science and how those essentially or affecting the way that we think about information about the careers and information. So one of the things I proposed to the top that I’ve had in mind for a little while talking about the disappearance of a scientist because I believe that this actually gives a bit of a roadmap, very, very useful, of where I think the technology is going, and where I think that ethics actually plays as part of that process. So can you see my screen? Yes, just bear with me, but bear with me. Yes, we can see your screen now. Yeah, okay. Alright, so small background I have worked in as the developers and Information Architect most recently have been working to help develop the offered through data science, machine learning, and kind of the general overall, the overarching rubric of what we call artificial intelligence. And unfortunately, I think that when, when you look at what exactly we think of data science and in the scientists, the question really kind of comes down to what the heck are they and why are they important? piece and more important question. If you are looking at getting into the field, what does that mean? What options do you have, and how does that impact? What you should think about in terms of your career developments, which ensures that the logger issues about how these impact not just technical fields, even nontech so one of the things I want to point out is that when we talk about data science, it’s been around for a while. The notion that data sciences this new field is something that I think that there there’s actually a certain degree of amnesia that occurs within the programming community. History. There are some wonderful, wonderful pieces, talking about the nature of information, programming then extends all the way back to the mid-1960s. When a lot of the capabilities for doing these kinds of processes. barely, even in their infancy, and yet the need to be able to manipulate data, we need to be able to find patterns and create models that have been around for at least that long, if not longer. So, in many cases, when we talk about data science, data scientists have actually been around in a number of gifts. Actually, more people that were responsible for looking at demographic data and trying to determine from that not only insurance information, but also establishing policy based upon the data that they were seeing more from, from surveys from census data from other similar types of information, marketing channels, statisticians in the whole field of sports, for example, has become much more data-driven just within the last 15 years or so. But even before then, the level, of skill necessary to be able to predict and determine the future of more sports teams are going to or which political teams

5:44
What is likely to be the future of elections and other things along those lines. Those areas were essentially in the state’s dish fight. And then now analyst is essentially someone who analyzes is not any big surprise. But at the same time, an analyst is someone who basically is looking at the information they have available to them. Tables are slowly moving into the computer age where they’re essentially trying to determine not only what are the patterns that have been in place, but can we use those patterns to determine the nature of the future in areas such as business, business analysts, technical analysts, the stock people who are working in research, most researchers, they simply need to have some kind of background in analytics, statistics and the ability to work with stochastics to be able to identify how particular populations of people or animals, businesses, essentially, so the notion of a data scientist has actually been around for a long time, however, or more to the point when we talk about this, even the tools that we have for data science go back for decades. A lot of people have largely forgotten about languages like Fortran. Fortran was really the first data science language out. It was a language that was essentially developed for able to handle not only basic computation but also to be able to handle additional work that came down to processing analyzing determining, creating models, and so forth, that created kind of a separate or away from the realm of business processes. Now, if you look at the evolution of computers, and computer programming, you find that Fortran largely kind of led itself into areas like saps and CRM system sophistical packaging. Eventually, you have programs like MATLAB and Mathematica, both of which were essentially designed to let you take the mathematics and the statistical information and statistical formula and then utilize steps to be able to produce and D juice from those models. An indication about how accurate information was and how readily be used to determine what future actions should take place. So with that, data scientists themselves and the term is not all that. In fact, if you look around the first usage of data scientist as a term only goes back to about 2000. And it was a different usage that you had data science is a concept. The notion of basically someone who’s focused purely on data science, purely data and data processing, is something that has only recently merged within about 2012. Interest doesn’t. And for a while, we essentially became kind of a point for a badge of honor. It was driven largely by the rise of computer language, our language, and our title. Our was actually kind of an open-source evolution of packages like SPS and SRSS. That essentially provided command-line utilities to be able to generate data to perform model generation found that Python originally started as a somewhat generalized language, but by the mid-2010s It becomes through the use of specific packages

10:47

To the others along those lines that have increasingly become the language for statistical manipulation to a significant extent, that is still true. And there is evidence that AR has kind of been declining in response to the rise of Python as a language. Now, what I can say from that is, you know, programming languages basically rise and fall all the time. Zeros, if you look at where certain languages are, strongly tend to cluster in specific areas Python, for instance, has become significant as kind of to the choice for working data analytics and his also by dint of the fact that it allows you to do certain additional mathematical processing has become critical in machine market chocolate into this. So in most of these cases, these particular tools, then essentially identified the data scientist as someone who was basically an expert in these tools. Now, there’s a what well known because it does determine the nature of the evolution of these particular fields and it’s an ongoing when you see there’s a distinction between a data scientist and a program a data scientist is essentially someone is creating models to be able to manipulate data or to to to analyze and more precise, that analysis process differentiates them from being programmers who are essentially building tools to be able to make this happen. So the tool builders are essentially programmers, the tool users are largely data scientists. That is also changing somewhat as we begin to move increasingly to models where machine language machine learning becomes prevalent. But for a lot of the purposes of distinction about what makes a data scientist you can readily say that a data scientist is going to be someone that is using statistical tools to be able to perform the analysis. Now, again, in addition to this, the notion that a data scientist is someone that basically is just a technical expert, these tools is something that is changing as well. So given that one of the key things that have evolved really within the last three to four years, although the foundations of it have been around since the early 1960s, has been the rise of neural networks and neural networks and machine learning are essentially two areas in which we are seeing the distinction evolve, arise between those people who are developing toolsets, or those people that are basically using statistical analysis to be able to determine behavior. And those people that are essentially attempting to create models, using the models themselves to be able to handle areas such as clustering for identification purposes, or identifying local minima. To be able, to determine where information or where you see the most optimal solution for particular jobs, more in areas where you’re dealing with sequential operations such as natural language processing, technology for text to speech manipulation, that effectively is a key part for any kind of natural language. Work. So those that shift from the statistical to the normal are basically changing.

15:41
The distinction between these two economists says one is old school data science and the other is new school science. And they do different things. I don’t think there’s any meal. You can say that neither one nor the other is actually less important in terms of their overall impact. It’s just that in one case, what you’re doing is looking at it from more of a statistical basis, whereas in the other case, you’re basically in the same high mathematics, some cases, even nonlinear mathematics to be able to build the models that determine behavior from that essentially evolve into the next generation of tools. And these are very exciting. I mean, when you start looking at this, you can basically identify things like image recognition, visual recognition, or video recognition, which is essentially a key quality to things like driving autonomous vehicles. Any type of categorization is increasingly done through natural language processing and the termination of things like chatbots are also being wedded to that. So in some respects, the field itself is already fractured, into distinct areas. So another phenomenon that basically has occurred is that we went from individual people coming in and being hired as data scientists usually pretty decent salaries. The development of the data science team and is actually a very interesting phenomenon that equation because if you’re looking for a future career in data science, you need to understand that it is increasingly something whether the specializations are becoming more important than the overall term. It’s kind of like saying in programming, I can call myself a programmer. But in point of fact, I happen to specialize in areas like user interface to side or back in graph data systems. Those specializations are basically what is determining the next generation of programmers but they’re also coming in the next-generation data scientists. Those include various data engineers, who are essentially the people that are responsible for taking the information or for gathering the information for processing, cleaning it, and putting it into forms that can then utilize more effectively. These are not statisticians, these, basically people that are much more interested in data quality assurance data, provenance, governance, but they are also people who are focused primarily on the pipeline of information systems. And you have the data analysts, these are the people that tend to think of as the model are the ones that are essentially generating the the the the models that affect how not only see the raw information process, but also that once they have those models in place, can be utilized to create new information to predict behaviors that can that in terms of not just okay, this is a number, but this is actually a program that is true in private other processes. So, those analysts are increasingly becoming important as people were building components in overall data organizations you have visualized. visualizers are essentially people that take data, which essentially are just constructed and make those constructs meaning, fortunately, an audience

20:04
Visualizer, visualizers are actually very important simply because we are reaching the stage now where it’s very difficult to be able to take a look at the data constructs that we have and make them meaningful to the average person. But if you have someone who can basically turn that information into a presentation, whether that’s his dashboards or other things like that, that’s becoming an increasingly useful skill. Additionally, you have areas like a storyboard artist or stream storytellers, who are responsible for generating interpretations of that data that in turn, will drive future business activity or future organizational activity. You have the programmers who are essentially building the toolsets, but they’re focused primarily on this region. So anyone centrally working on areas like GPT three, which is, which is Google’s next-generation, quote-unquote AI package for determining language processing. Those are people that are building out the tools, but they are working very closely with data teams to be able to determine what tools to work with. Finally, you are increasingly seeing the business strategist or the AI strategist who is essentially responsible for taking the nation and making sure that it works well to further the overall corporate aims for Global’s that are the data itself supports. And this is kind of important because if you don’t have someone that’s basically acting as an orchestrator and to be able to say this is the kind of data and this is how we need to get it this is why we need to get it and this is what then basically, all of the other processes are essentially just so you need to have someone who looks at this from a higher level and say, why are we doing this? And these are additional areas where we’ve been getting into data governance strategies or data emphasis. The notion that we actually have an ethicist as the position is actually one that I find vastly means among other things, that we now can actually only for us, for us to do work and anyone who happens to be a philosophy major, but a notion that you have to be able to look at the meaning of data and why you work dazzling, what are you going to use it for? And how can you determine there are minimal biases possible is going to become a very key area for organizations moving forward. There has been a lot of work has been done. To be correct. quite honest, it is cheap. You know, when you go out and say, Okay, can I use this information to affect social institutions or political institutions? Can I use it to basically change the way people vote or even to suppress the way that people do? Those are areas where you need to have someone who basically acts as the consciousness of an organization but also is someone who understands the meaning and purpose and value of that data and why you have to be very careful together. It is different from being someone that’s managing privacy, although there’s a lot of overlap. The data ethicist is essentially someone who is concerned about the information that is available but is also concerned about being available to different stakeholders. But is also concerned about is this something that you should be doing and this has become an increasingly important aspect of the sciences for So finally,

24:48
I think data science is a consequence. For dispersing the specialists, the subject matter experts. In the governor’s champions, those roles become very critical. Increasingly, there are very few roles within an organization that does not in some way, shape, or form. Get impacted by the utilization of sanctions themselves. We are all in becoming in science. Finally, to wrap this up, I think the future that we talk a lot about AI AI becomes the buzzword, but the reality is that AI generally has only been successful in those cases where there is a significant amount of human intervention that is also involved. So increasingly, I am seeing thinkers influencers, and those that are basically working with the future creatures of this particular recurring technology, thinking increasingly about AI not as artificial intelligence but as augmented intelligence and augment intelligence basically, that a lot of the issues that we are talking about a lot of the capabilities that this current wave of technology provides to us give us significantly more ability for that because we have more ability. We also have more responsibility. Going back to Spider-Man, with great, great power comes great responsibility when that’s happening. We are becoming more and more powerful in regard to gardening station data, can we are becoming more powerful to the extent that when we talk about that situation, we need to understand that every action will have an impact upon not just ourselves or our organizations. And so, when we get into this whole notion about data about the role of the data scientist and about augmented intelligence, we have to understand that we are now becoming increasingly responsible for the impacts that we are creating, for society, education, and various disadvantaged groups. All of these come into play for the environment. These are all issues. So I think maybe towards the end, presentation.

27:46
A presentation, Bert and I think you brought up some great points in terms of some Grayson’s passion on how the role of the data scientist is changing and how they are becoming more from the generalists to this specialized you know, different areas of specialty. And so, what is your advice in terms of you know, when, when people are getting into data science, should they first start as a generalist and then get into a specialization or start with a specialization all at once?

28:27
It doesn’t hurt to gain enough understanding about the process of science and other aspects and I don’t even necessarily call it the sciences. I think we’re in the intelligence realm. But anyone going into these fields probably needs to have at least an understanding of the various different opponents, standards, graph theory, machines, learning, neural networks, so forth, and so on. Make these up. That they can have a more effective decision as they enter their career about what they want because you can’t do it all. Absolutely.

29:30
Thank you, God. And I want to let the audience know that if you have more questions for Curt on LinkedIn, and we have this live stream there, so tag him and he’ll be able to answer any more questions that you have for him. Great points and take advantage of his experience and wisdom. And he’s the managing director of data science Central. He’s written many books so obviously, he is, is he is the big deal here. So thank you, God. time.

30:04
Take care. Bye.

0:15
Hello, everyone. Good afternoon. Good evening. Thank you for joining us in the afternoon session of day one AI diet world 2021 What a wonderful lineup of speakers we had in the morning and excellent attendee questions. That was fun. We heard from some great speakers including Christina from IBM. And now I’m super excited to hear from Betsy, another great speaker from IBM. What do you know? IBM rocks right. So, let’s, let’s give it away. Let’s hear from Betsy. Let me introduce her. Betsy. Your talk is vice president of ethics and policy in the chief privacy officer for IBM. She works closely with the IBM AI ethics board developing principles, practices policy, driving that people develop development, deadlock deployment of technology. After joining the CPU, FC was senior counsel for the idea and lead field in providing business unit support for IBM Global managers, acquisitions, divestitures, IP partnerships, and other complex Alliance transactions. She has provided legal support for various IBM practices and offerings including OEM channels, sales, cloud, and cognitive systems, power systems. And the AI X operating system. Wow, super excited to have you, Betsy. Oh, thank you, Sofia. Thank you for inviting me. I’m very happy to be here. And it should be said I am vice president of ethics and policy at IBM reporting to our chief privacy officer. And I’m going to start off by saying I’m going to share a lot of examples from IBM, but your thoughts and opinions here are mines. They don’t necessarily reflect the position of my employer, IBM, so if you’re upset with anything I say here, you can contact me. But that said, hopefully, you’re able to listen to some of the speakers earlier today. And Christina, my boss discusses betting trust and all that you do. They haven’t been discussed the importance of diversity and inclusion in data in Analytics, which has some very nice parallels to tech ethics and creating a culture that embraces ticket. And then we also heard from that panel regarding the importance of whistleblowers and encouraging people to speak up so I want to thank those speakers in the panel for laying such a great foundation here. And now I want to pull on some of the threads that were laid out in those discussions and dive deeper into how you can operationalize the policies regarding the ethical development or deployment of AI and other technologies and strive to create a culture that not only embraces the policies that advocates for the values behind those policies, and includes the voices of the many and not the few. So first, I want to point out that many people want to put tick ethics into the same box as other regulated areas related to business ethics.

3:44
Frequently hear a comparative privacy report into the CBO. And there’s a lot of similarities, but they’ll compare it to privacy and the GDPR. And all the regulations that followed GDPR and then I’m hurting other people who assume tech ethics is just another flavor of behaviors to be regulated by anti-bribery and anti-corruption laws like the Foreign Corrupt Practices Act or Sarbanes Oxley and comparison to those regulated activities aren’t wrong there’s a lot of parallels to be had, as we’re basically talking about a specialty or sub-specialty within business ethics. And we know that this is an area that will be regulated, and in fact, in some industries, it already is regulated to some extent. So I’ll also suggest viewing that while the regulations, they’re not what’s going to drive the corporate culture, it’s values that drive the corporate culture. And so while regulations are more about what you have to do, values are more about what you do just because you know, it’s the right thing. And while regulations frequently project or reflect our values, they also typically trail innovation and emerging technologies. So there’s a practical side to making sure that your values influence the design and development of these new technologies. And I’ll get to that in just so while creating a values-based culture is complex for today, I want to focus on three basic concepts that can help drive and determine a corporate culture. The first is this culture needs to have a strong foundation of values. Leaders can’t fake their way through tech ethics. Your employees and your customers are passionate about this topic. And as noted by many of the other speakers today, leadership needs to embrace that passion. That passion-driven leadership then needs to enable the stakeholders to speak up. And really almost anyone can be a stakeholder, but you have to give them a platform so their voices can be heard. And once they’re speaking up, then the leaders need to engage in active listening and further actually listen with action so let me try to explain why I really like these focus areas. So as I said, for a company to build a values-based approach to managing its technology offerings, the leadership must embrace and advocate for those values. It’s imperative that voices from the top advocate for good tech and responsible innovation. In studying this topic of creating a culture I looked back at some of the challenges faced by HR as they work to create corporate cultures that embrace diversity and inclusion. And again, I found several parallels Ted Charles, who was a former Vice President of Global work, workforce diversity here at IBM once said, passion is contagious and when combined with leadership, the equation is effective. And I find this to be really true when addressing tech ethics. as well. People tend to be very passionate about doing the right thing about improving life and striving to benefit the many. They don’t always agree on how to accomplish this, but they share a passion for it. Other speakers today have given you many examples of the amazing things we can accomplish with AI and other technologies. And you’ve also seen that people have a strong desire to avoid harm or the pitfalls that can come if we don’t address innovation responsibly. Companies will lose the trust of their clients and increase the mistrust within society for these new technologies that can stifle their development in use. And I’m guessing you wouldn’t be here today. If you haven’t at least witnessed that passion for good tech. And now you’re either curious as to what it’s all about or better. You have that passion yourself. So move beyond the discussion of people in general who are passionate about tech ethics, and recognize that these people are your employees, they are your customers, and they are the people who will be impacted by whatever technology you’re bringing to the marketplace. With that in mind, if your leadership is not embracing the principles behind the responsible and ethical use of AI, you’ll have a very hard time creating a culture that also embraces those principles. And in my opinion, you’ll have been creating a real disadvantage for your company in the marketplace. So those leaders who do embrace the values, need to be able to draw others into the discussion and be the catalyst that sparks discussion and convinces others to always strive for innovation that benefits the Mini and mitigates the harm.

8:46
The topic is complex is tech ethics cannot be forged from the top alone. It involves determining what is beneficial, what is fair, and asking ourselves, are we doing the right thing? Yet you ask 10 people these questions and you’re going to get 10 different answers. And answers will vary based on people’s economic, social, political, religious, and geographic backgrounds. Answers will also vary based on the person’s relation to the technology at issue. Are they a developer? Are they the user? Are they the seller are they the person who will be impacted by the result? With this many viewpoints and stakeholders but corporations, policies and processes need to be constructed such that they encourage multi-directional, multicultural conversations? In other words, you have to empower the voices so that the conversation flows both from the top-down and from the bottom up. Just as the technology is constantly evolving, so too is the need to address the ethical use of that technology and creating the ability to have this continuous feedback and enabling the ability to adjust to changes in the environment is key to sustaining the conversations and staying abreast of what the next issue will be. So as you may have heard from Christina this morning, IBM has established an AI ethics board made up of leaders across the company. And underneath that board, we have a network of focal points that help the board to engage with all employees. These focal points are from different business units, different corporate functions, and they’re also sitting in different geographies, to try to create as much diversity and inclusion in our board and in our focal points as we can. Equally important several community groups formed organically within IBM to address the questions of Tech Tech ethics, these are people who make connections across IBM. They were united in their passion for ethical issues, and they ended up creating real-world solutions with their crowd sourcing-like groups. And this is no small movement and includes hundreds of employees from over 45 countries.

11:11
At the same time that I’m seeing all of this passion and that spark of kind of a grassroots movement within the company. All of this networking was intentionally designed to create opportunities to be heard. I still also find that people sometimes fail to connect the dots and they fail to see how they can make a difference. And that’s where education and training play a critical role in opening up the doors for discussions. So similar to what our diversity inclusion team does, we spend time training IBM ORS on tech ethics and encouraging every employee to be an upstander and an ally in terms you may hear in the diversity and inclusion arena. But after one of these training, a developer who is a person of color reached out to me and he said these taken all of the diversity inclusion training that IBM offers and he gets the need to be an upstander in the workplace to speak out against any harassment and bullying and discrimination that he might see. That he then confessed to me that he really hadn’t thought about it in terms of speaking out, should you see an ethical issue around technology? He said I put on my developer hat. And I’m focused on solving the technical problem before me. And I don’t always think to look at it in the context of fairness or bias. So this says to me that even if your company is wildly successful at enacting DNI policies, and even if you have other policies, encouraging employees to speak up if they see something wrong, you have to make sure that you’re going that extra step to say that it’s not just about how people treat other people, but it’s also about how technology will impact human life. And regardless of what your role is, we want to hear from you. So that’s one example. Another conversation I had following one of these sessions is a data scientist spoke up to me after the presentation, and he said, Betsy, we’re data scientists, we don’t know anything about ethics. And so I asked him, I said, Do you have a general sense of how to treat people fairly? of what it means to include people of different backgrounds? And what it means to show respect for people and for their privacy? And he, of course, responded, yes. And so I said, well, then you do know something about ethics. And what it tells me is that people just need to know enough to enable them to ask good questions, and they need to feel confident in that knowledge. And that’s what this whole effort to promote tech ethics is all about. It’s about making space for people to be curious, and to ask those probing questions that will really test if the technology is in fact creating a beneficial impact and if we’re effectively addressing and mitigating any risks. So that said, the question I probably get asked the most is, can we provide you with a checklist? And I always caution against the use of a checklist. A checklist implies that once you’ve checked off everything on the list, you’re good to go. No further discussion is needed. At best, I can give you a list of considerations. I can give you a list of types of AI that frequently raised issues and you can look at the draft EU regulation to construct a similar list. Or I can give you a list of design and implementation issues, such as has it been tested for bias? Can you explain how it derives recommendations? Are you being transparent about the accuracy or what data has been used? But when you get to the end of these lists, I’m still going to ask you what have we not thought of? What is unique about this particular use case that might warrant further consideration.

15:19
This request for a checklist reminds me of something Ginni Rometty, who’s the former CEO of IBM has said many times, and that is for growth to happen. You have to get comfortable being uncomfortable. A checklist brings you comfort. You know that feeling you get I’ve completed all of the items that I need to do. But because these are emerging technologies, along with all the new possibilities come new problems that we can’t easily or always anticipate. And that requires you to leave your comfort zone and to really tackle the tough questions. And when I find that resonates well with the technical communities that I work with, work with is to tell them I can give you a list, but it’s not a checklist. It’s a list that is really meant to drive your curiosity. I need you to stay curious about the full impact of the technology and the innovations you’re developing. And that’s something that relates to and that’s something they can get excited about. So finally, once you get your T’s up and actively engaging with you, you have to engage in active listening. People need to know that they’ve been heard and when and where appropriate. They need to see that you will make changes or take other actions that demonstrate that your policies are not just a bunch of words with no meaning. A very visible example of that was when IBM announced it opposes the use of facial recognition for mass surveillance and racial profiling. But for every big headline, you see like that, there are many other smaller actions that we take in response to someone raising an issue. Someone asking those good questions, and we find that it’s actually pretty rare that we need to stop the project completely. It’s really more about fine-tuning it and putting appropriate guardrails in place. IBM has at least 10 formal avenues for somebody to speak and the ethics board has around for teams to bring use cases. But in my experience, some of the most impactful listenings have come from one on one discussions. So every time I lead an education session, I encourage them to reach out to me directly. I invite the conversation and that brings me back to finding those passionate leaders in your company. Encourage the conversation and the feedback loops. And most importantly, stay curious. So she’ll be with that. I will turn it back to you and open up the floor for questions. I guess they are as the green screen I don’t know why this field suddenly decided to come, Sam, can you take over?

18:14
Yes or no problem? Present. Good. Fantastic. So just something to look at some q&a. Just looking at the audience. Do we have any questions for you? And why should the organizers cover? Yeah, thank you so much. I think it’s a really really important point that you made earlier.

18:39
We need to involve a cross-section of groups and stakeholders to build ethical AI and technology and that leaders need to be on board desperately. Do we have any questions from the group? Let’s

18:55
See. Okay. I think we’re just waiting for a few to come through Betsy. So

19:06
Just give people time. Yeah. And I mean, how do you implement something from Christina sock earlier she mentioned it’s important to refer to IBM Policy Lab and the work you’re doing there. And that trust is important. You know, from your perspective, how do you build trust and that cross-section of individuals, and is there a formula or winning formula yours? That was the best approach. It’s more of a matrix and I would say a winning formula. And it is, it really is about that two-way conversation. So you do have to have passion from the leadership at the top. I really agreed with Katherine on that. Talk earlier today about diversity, inclusion, meaning that that you have to have buy-in from your leadership and they have to really be the advocates. But then you have to engage we engage with our customers and solicit their feedback. On what they’re looking for and what trust means to them. Engage with our partners, people that we have joint development agreements with, we engage with universities, policymakers, so we reach out to as many people within the community within IBM and outside IBM and constantly invite that conversation going because that’s the only way that you can even begin to predict what may be coming both in terms of what is coming with the technology and then what ethical issues to arise from them and get the true feedback and then decide how to act in it. You have to be able to pivot very quickly. And if you’re if you kind of let your foot off the gas, then you’re gonna miss something. So yeah, it’s very much about holding as many conversations as you can. And I have found that one on one taking the leaders within the company who are passionate about it and opportunities to engage in one on one conversations with employees. With some of our senior leaders like Rob Thomas and Arvind Krishna. They have Slack channels. And employees can reach out to them directly and it’s actually valuable to open those doors. Right. Oh, great. Thanks very much. A sec. Let’s just check we have any further questions? Just bear with us? Yeah, we have actually had one from Susanna and my question was, how do we encourage engineers as quick loans gonna go to our feeds? Yeah. How do we encourage engineers to think beyond the technical side of a problem when it comes to approaching it? Yeah, so that’s pretty much that example that I was surprised when I got that question from a developer, I mentioned. But you do have to do the education. So IBM has policies that were put out for Your Business Conduct Guidelines. For as long as I can. Remember, since I’ve worked at IBM and they say, report any ethical issues you may see, and here’s how you can report them. And they weren’t the engineers who were not making the connection. They were only thinking about ethical issues of people interacting in the office. So you actually do have to go to the engineers and you know, of course, a lot of them get because that’s where these grassroots communities that come up with IBM inventors that are really tackling these problems. But you have to go to the engineers and say, I don’t care what your budget is. I don’t care what your timeline is, your deadline is and this has to be supported by management all the way up. Which it is here. And so I want you to take off your developer hat and what you put on your personal hat. Think about how is this technology going to impact society? I know you have a good intention when you designed it, but you really have to step away. It’s kind of like how, if you’ve written a paper, and it’s always best to turn it over as somebody else do a pre-free guide and they catch things that you didn’t catch. And I call it 360 reviews. So you come up with a technology to try and solve the problem. Don’t pick it up and look at it from every angle you can think of and invite others to come at it and really explore what you might be missing. But you do I find you have to engage very directly with your engineers, your developers because they put on their scientist’s hat and they don’t always remember to look at it from a different angle. A great point. Absolutely. I’d agree with that. 2% And often we find ourselves in roles, you know, we’re so focused on what we were doing as engineers and product managers

24:02
Within our teams and individuals that we need to get out and have those conversations and whether that’s some collaboration platforms. Once once. Brilliant, super. We’re on 25 paths to see. I’ve got time for just one more question. Maybe Betsy, how do you feel I allow you for time? Okay. Okay, so we have another question for me. Let’s just see. So I think this question comes from Alan. question is does IBM partner with other leaders in the tech industry space and particularly in academia? why they’re so that’s another question that we do we have partnerships with MIT with Notre Dame. It was Stanford in the academic and likely other universities, many university partnerships, and those are the three that I engage with frequently, but there might be more than one missing. We have signed off with the wrong call for ethics so engaged with the Vatican and the Vatican is now expanding their program out to other universities. So kind of an indirect reach out that way. But then we also partner with many of our customers who are interested in similar ethical issues, and many of the partners that we, that we sell with and create solutions with so yeah, it’s multifaceted. Yeah. Yeah. Wonderful. Fantastic. Thank you so much. Right. I think if I’m just looking at a fee because we do have a new question. I think that might be the last one. Yeah. Okay. Thank you very much. Thank you. Very welcome and done well, the voice of the customer producer. Will, we’ll reach out to fantastic thank you so much. Thank you brilliant. Speak to you soon. Take care.

0:37
Let me introduce her really quickly and then we’ll bring up bring her up on stage. Catherine came words passionately to provide senior executives with the hottest content and insights in the areas of digital data analytics information, business development, and innovation. She hosts and moderates Corinium’s largest events as well as the business of data pop. Please welcome Catherine cane. Sophie. Hello, how’s it going? It’s going, how are you? I’m good. I’ve got a bit of a cold. So I’m going to apologize now to everyone that I sound a bit funny. It’s not just the British accent. It’s a bit of a cold as well. But thank you for having me today. Yeah, you’re welcome. This is so great. And sorry to keep you waiting in the green room. I know we get super excited some of our speakers and audience had such great questions and so we went a little over time. But I’ll add your deck to the screen. And take it away. Amazing. Thank you. No, I was absolutely floored. All things sustainability. Definitely. An area of mine for sure. But you gave me a fantastic interest. I’m not going to spend too much time expanding on what I do. But yes, I do. Look after our video content and some of our audio content as well as podcasts as you mentioned that I do get to moderate lots of events. And it’s great to be with you today. And I can see the comments below so if you do have any questions do pop them in but I know we’re gonna have a bit of time in the end if you do have any questions. So today I’m going to talk you through across the last year within the business of data, how we have been elevating voices within data and analytics and making our content more inclusive and therefore competitive. But I think probably best is if I start outlining a bit more about who we are. So you have a bit of context. So Corinium is a successful conference and event company holding portfolios and data analytics, information security, and AI. We have hundreds of people all around the world, generating multi-day conferences through to customer intimate round tables. And then the business of data is our data and analytics platform where we host our content. So this is where the podcast lives. We have report articles, blogs, you name it, we’ve got it. That’s where and then over the last year whilst we’ve been forming the basis of data we’ve been on a bit of ambition to ensure that we create amazing content where we elevate all voices within the community and do so lucidly. Now we’re going to take a moment to pause here and say, Are we perfect? Absolutely not alike. And then diversity. Inclusion is a journey. Not a destination is always more than doing and I’m sure everyone in the audience will agree that there’s always more that can be done. But today, I’d like to talk through what we have achieved so far, and how we’ve done it. And the case studies I’m going to share with you today diversity. However, joining us here today, I do believe that the learning points in the data governance privacy ethics, I know we’ve already spoken about today. So I’m going to be picking all three examples in your interest of time otherwise, I could go on and on about drawing three foundations for you to do the same thing in your organization. Hopefully, you can take something from this little talk here today and start implementing it in your organization. Now, before we do that, I just want to center us and talk a little bit about why we’re doing this. Now in the previous talk that I’ve mentioned, ROI and all those things. I think it’s important that we discuss why we ought to be doing things now. Obviously, the first reason is that it’s good to do good. But at the end of the day, there are so many good things that businesses can do. So why should you do this? I mean, again, we’re talking about sustainability another fantastic thing, but in my beliefs, diversity and inclusion are absolutely fundamental to continue business revenue. If all content was generated by people who all look the same, from the same background perspective, we’d only be serving a segment of our community. And to be honest, the content would just be boring, but having a wide range of perspectives that allows us to generate competitive content, and in return that brings revenue because all partners want to be part of great content. So as you can see, by being inclusive by being diverse, we’re creating stuff. So just as practitioners yourselves can develop models with the best we can generate better content.

5:41
Now I know and I’m gonna put it in here, that maybe some people do need to supplement that diversity and inclusion can be regarded as a soft, fluffy topic. But more and more organizations are waking up to realize that they really are leaving money on the table. When they leave discussions. So now we know what we’ve been up to. So let’s start with those case studies. So the first is our leading female executive monthly discussions. So these are in the form of LinkedIn live sessions each month, a group A-C suite, and senior females gather to discuss a hot topic. So so far, we’ve covered cloud AI, data, culture data for good and so many more. We just recently had our anniversary and since we’re doing this for over a year now. And you can watch these on LinkedIn live or you can pop over to the data platform. Now, you’ll notice that I really, there’s no mention of being a woman and apart from our special International Women’s Day discussion, we don’t expect our panelists to segment themselves and talk about their gender and their role at work. Rather Our aim is to elevate the voices, providing a platform and visibility to others of these amazing executives to their goals and experience. Now, this is what I would call diversity inclusion in action rather than words. Now, I’m going to say of course there is absolute merit in the discussion but how women experience the world differently in our community. But so so much of a believer that it shouldn’t be the topic that we are invited to speak on as well. So what are the outcomes of this activity? Well for these women, they told me that process participation, participation has meant the best and a great opportunity to discuss interesting features of consultants and say, Hey, what’s interesting to you at the moment, and they tell us now, one of the things that I’m proud to say and I’m fairly confident in saying one of the items has been to provide an example of amazing real-life examples of women in very, very senior roles, some of the world’s best-known brands, absolutely succeeding in their careers. Now, this is a break from the traditional image of what a data analytics leader looks like. And I’m really, really proud to be part of that. Now, this work is so important as you know, it’s important to show the talent market what is out there because we know that diverse talent is required in order to overcome some of the biggest challenges that our world faces today. So biases mentioned in the conference already, we are not going to overcome some of these challenges if you do not have a diverse talent pool in the room solving. So that’s the first example. Now the second example I’m going to give you today is our 30. Leaders under 30 report. This was pioneered by our head of European marketing. Earlier this year, we launched this nomination process. It was the first one we got it done segmented by age. The idea behind this was to shed a light upon the leaders of the future experiences which will be unique to the audience we primarily cater towards. And again, it’s important to do this work. Because the primary audience that we do serve is senior executives. And naturally, that means an older cohort. But providing a project like this, to generate perspective and understand what’s next in the generation of leaders who are coming up. What are they doing to make the community better, what are they skilling selves up to be? And what was really interesting is throughout this project and process, it was really clear that a big part of these people’s success, the top 30 all had something in common. And that was taking advantage of mentorship. So one of the things we did was create a community shout feature in our weekly pay stream, which we do every Friday where people can come up to the virtual stage and show what they’re doing. So lots of people use this to shout about a job opening or looking for a job. Others use it to show by posting on LinkedIn, they want some support. But we really encourage people to say that they’re looking for a mentor, that they are themselves open to mentorship because we know the leaders of the future in order to again have that diverse leadership that we really, really, really require that we know that mentorship is a huge part.

10:19
Now the final example I want to get today is a little bit different from the other two, this is more the inclusion side than the diversity side. And this was born out of a chat between myself and our Head of Marketing Global Marketing Officer. We were discussing over a coffee at one of the studios recording our business data festival. how so many people consume content in a different way since the pandemic and I happen to mention recently that I took up listening to audiobooks personally I struggled to retain information Les Brown in a long written format, podcast video interview, or audiobook. So at that moment, we came up with the idea of having someone voice-over and an upcoming report to provide a different way of consuming our written content and for it to be more accessible. Given the industry rise in podcasting. To be honest, our own podcast success, we felt that this was a pretty good idea to at least try. And the results have been really interesting for this first attempt. It’s something we definitely want to repeat in the future. And it’s given us a new chance for our audience to engage with us in a new way they haven’t been able to before and something I mentioned at the start. It’s also given us that chance to be competitive. Although podcasts are incredibly popular, not too many people are producing audio reports in the data analytics space. So by being inclusive with being competitive, in turn, that was rapidly so just from those three examples, I chose three out of the many because I thought they weren’t they were really really good examples of what we’re up to. So what can you take from these case studies and apply them to yourself so there are a few key things that I think are really fundamental for elevating voices? And again, I’d love our audience members here today to be thinking about their own organization’s own departments, their own communities, how you can make sure you are being as inclusive, inclusive as possible, encouraging diverse audience participation, speakership, whatever is your how you can do it. So the first big challenge is infrastructure. One of the biggest pain points of our community is not having the knowledge of where to participate within that passion. Infrastructure is so so key. So because we have a monthly discussion, many women have contacted me asking if they can participate in the leading executive panel or they want to be regular audience members, or can you send me a calendar invite because they believe in what we’re trying to achieve by elevating female voices organically. Now, this is what I alluded to earlier, but I really want to hear that you really start to attract people based upon your action values. Your action is so crucial. So whether that is a forum, a discussion, a committee, advisory board, whatever it is, there needs to be a level of infrastructure where people can come and live out their values with you. So I get I keep saying it, but this doesn’t have to be diversity and inclusion. This could be, for example, take ethics, ethical AI to talk about that. Why not create a monthly forum where you can get together and chat about the concerns the opportunities, excitement, challenges confirm arrange of guests, get involved, encourage discussion, but most importantly, have a place where that discussion can occur. There is a massive difference between a company that’s concerned about diversity and inclusion, and a company that actively provides a place to hear those concerns. So that’s the first one. The second is naturally this type of work also prompts people to talk about your organization. Now, this is great for recruitment, but it’s also great for networking introductions. So I’ve had many leaders request to be a guest on the business of data podcast on the back of listening to episodes with liking data, or my skin my story. Now, these haven’t always led to another diversity and inclusion discussion. It’s just that their personal values align to what business actions are that they can say, Yes, I want to be part of that story. And whenever it comes to brand awareness, I always think of an old friend of mine who said, the media doesn’t know if I take you haven’t done it. And I absolutely believe that’s not cool. It’s all well and good doing these activities in cycles and eternity. But unless you spread the awareness, the good stops that it can’t go any further. So as I mentioned earlier, we’ve had introductions to other leaders, people nominating themselves to get involved, and just general gain and positive sentiment, which all enables us to continue doing the work we’re doing because it’s like a chain reaction that everyone wants to get involved.

14:56
And then the last thing, which I think is just on every single slide deck ever of any team trying to get something which is having champions, it’s absolutely crucial. Now, I’m a firm believer that we work with championships shouldn’t go to one person all the time, but it is so so important that you identify who is really passionate about this and then support them. You can’t just identify them and say who’s passionate, you have to support them internally and externally as well. And again, you can use any topic here so whatever it is that you’re trying to push forward, find those champions, high-security ethics, governments, whatever it is you’re trying to achieve. Find those champions. Now I have to do a shout-out to our senior leadership team who have always supported our initiatives towards elevating voices and data analytics. Whatever I do is we come to them. They say run with it, do it, experiment with it, which is such a fantastic place to be because, without exception, you have a lot of passion and notice. And again, that goes back to the challenge around not having infrastructure and not being able to do it. So I’m going to wrap up very shortly, but my final words would be don’t be afraid to be passionate. It attracts other people to you. provide a place for that passion. Don’t go keeping it to yourself, spread the message and reinforce your champions with resources and support. Now the last thing I will say is if we’ve got any questions that have come in, the last thing I’ll say is we are we’re just about to end nominations for the top 100 global leaders list, but you can still get yourself and that will be a huge celebration for everyone. If you’d like to. Yeah, sure. Thank you, Katherine, this was awesome. I loved all your examples. Thank you for sharing the idea to see this record. I think that’s someone from the premium team, but it’s a great idea to see this report, Katherine, and how the 30 130 leaders are seeing the importance and benefits of mentorship and becoming mentors themselves. Yeah, one of the questions I get quite often on this topic is what we’re so lucky to have and not everyone has. What do you do if you don’t have executives? And I think it’s, it’s demonstrating the value it provides in a sense of being competitive. So as I mentioned, with the report with the audit report as well, it’s about staying well, if we go the extra mile we do these things, we’re going to be competitive, and it’s going to be advantageous to that ROI, which is today we’re all businesses really really care about. So it elevates voices it does that. That’s the human good, but it also helps for money as well. Yeah, I loved your spin on the report. Putting it in an audio form, right? I mean, we are all trying new things. And I know that when we are either active or doing things then we can behave still the auditory things going so we listen to Apple or I think that’s a great spin. And I also agree to be a marketer, lifelong marketer myself, I agree with you, the media doesn’t know it, then you’re not done enough, right. It’s great to be a champion within the company, but sometimes you have to champion it outside of the company also. And also I think that helps to bring stakeholders on board sometimes because sometimes they are not convinced of the idea themselves, but when they see the need and the enthusiasm outside then they are convinced that this is a good idea. So show them instead of data always speaks volumes. So that 100% I mean, it’s statistics like the fact that it’s something crazy like 95% of mobile users don’t listen to the video with signed on, they watch it but it captures because perhaps they’re in the office looking at their phone or should be meeting.

19:15
There’s an inclusive comment that we have captions on videos for the deaf community and hard of hearing, but actually is the statistic that the majority of our community are watching videos without captions goes through. It’s absolutely behind. It does need to be data-driven. And if it takes that statistic to be the one that pushes the budget over the edge so that you can get captions on your video, then that’s what you need to do. Sometimes you need to think outside the box and it goes back to my earlier comment about diversity and inclusion being seen as Fluffy, fluffy topics. So it doesn’t need to be recombined with different kinds of what still get the same result. You don’t have to always put it under the inclusion. It can be under the marketing banner can be under the engagement value, whatever it needs to be the results. One last question for you, Katherine. I know we have great participant three questions. Quick question for you had executed by and but what do you advise? Yeah, okay. So I think you already answered that question. Yeah. So that one comes in. It’s such a common one. And I think it relates to my earlier there are so many organizations that are waking up to this, but I do feel very sorry for those who are still in those organizations who perhaps haven’t quite woken up. But I do think the pandemic will have worked wonders for thinking outside the box and those organizations who, who perhaps couldn’t get on board with nontraditional thinking, if you want to call it that, I think so. Yeah. Katherine has access to that from undersea. Absolutely, exactly. Very much so and then you can get definitely go down the path of translation as well with captions allowing people who aren’t speaking the same language and we do this within the cranium and some of our Brazil and Mexico based events where you can join in and you can have an English speaker speaking to a Spanish speaking audience and do all those fun things. So again, really proud of where I do really champion this going forward. Thank you. It has been a pleasure to catch Okay, thanks so much.

 

0:07
Alright, looks like we have Eric!

0:10
Let me introduce my next guest and speaker. Eric Corey Freed is a brilliant architect, author, and global speaker. As Senior Vice President of sustainability for Canon design. He leads the healthcare, education, and commercial teams towards better and higher-performing buildings for over 20 million square feet. A year. For two decades. He was the founding principal of organic architecture, a visionary design leader in biophilic and regenerative design. Eric is the author of 12 books, including green building and remodeling for dummies. He holds a prestigious LEED Fellow award from the US Green Building Council. Please welcome Eric. Everybody! Hey Eric. How are you? Good. How are you?

1:12
Good. You are right here backstage in the green room. What am I supposed to shuffle the agenda you’re like? And guess what? I’m glad you’re here. And the stage is all yours. Take it away.

1:29
Thank you. Hopefully, everybody can see my screen. So thank you for that lovely introduction. As you heard, my name is Eric. I’m an architect and director of sustainable design. If you want to email me or connect with me, you can do that. Also, if you want a copy of the slides, you can text the word green to that number. And, and I’ll send you the slides. By the way, that’s not my number. So don’t send me weird pictures of your junk or anything. That’s just an automated service that we use. So keep that in mind. Also, normally I live in Portland, Oregon. Today I’m in LA oddly enough, but normally I live in Portland, Oregon. I think what we’ve all seen in the last maybe three years is that nowhere is safe. Now climate change has come for everyone. And disproportionately of course, but no matter where you are, there are risks and threats. Over the last few years. I know that we’ve all been like in our little hidey-hole right in stuck at home with pandemic and I’m not good with staying home I like to go out and so I started developing these weird hobbies I started collecting pictures of people that were completely unaware of their face on Zoom. And I’ve got 1000’s of these now.  What else,  I applied to be an astronaut. This is true. And I have not heard back. So. Yeah. And then I wrote my 12th book on the circular economy and the reason why I wrote it on the circular economy is that I really see that as the framework. We need to address the problems that you face. If the traditional economy is what we call linear, take make the waste economy, circular economy loops and make waste into harvest make and remake for over again. And companies like Adidas, and IKEA, and Amazon, and Patagonian Lego have all embraced this. And we got to interview all of them for the book, which was kind of wonderful. So given that context, where do we go from here now? Right? We just spent almost two years indoors. So I want to ask you, what was the biggest driver of digital transformation at your company this year? Was it that your CEO had some grand vision that he or she wanted to put out? Was it that your CTO deployed some new amazing game-changing technology? Or was it more likely this little guy because I’m willing to bet it was that little guy? If anything, the good news is the COVID showed us that we could work remotely from anywhere pretty well. And if there’s any light at the end of the tunnel, maybe that’s it. So today what I want to talk to you about very quickly, and I think I was given six minutes or something to share this is I want to talk about the urgency of carbon and this is me as an architect. From my perspective, I’m not a tech person. Number two, how we redefine cost and a little bit of our innovative approach and how we get into it. I want to start with the urgency because the urgency of Harvard is now desire for lack of a better word.

4:28
In August, the IPCC came out with its sixth and final assessment report. Essentially unequivocal data from all the world scientists about 200 scientists all in unison, saying this is a flashing Code Red for humanity. One of the little tidbits in the report was that we’ve already released enough carbon in the air to blow past our 1.5 degrees centigrade. targets. That’s the goal that we’ve all been working towards for the last 30 years. Well, it looks like we might not make it unless we take really, really urgent action today or yesterday. Really, as an architect, we tend to think of one building at a time but one of the things that really changed for me in the last 510 years, is that I don’t really see it one building at a time. I don’t think we’re going to solve the world’s challenges one building at a time, we really need to realize that a building is actually connected to a larger network of infrastructure and legacy systems and communities and history and, and neighborhoods. And really, if we want to address these problems, at the scale, we need to address that we need to not think about one building at a time but rather in this district scale. Because a lot of our buildings are built based on a set of assumptions that are no longer true. And maybe they never were right. In a warmer was the world. How can we design buildings the same way that we’ve always been designing? Because it really makes sense. We’re, you know, we’re working on projects in Miami Beach and we told the client, we’re not designing your air conditioning system for 2021. We’re designing your air conditioning system for 2050 Because it’s only going to get hotter and stickier in Miami. In addition, all that the world is now building at an unprecedented rate. We’re about to double the world-building stuff by 2060. To give you a sense of scale, that’s essentially building an area the size of New York City every five weeks for the next 40 years. That’s what we’re doing. So imagine a New York City area being built every five weeks that’s what’s going on. And if we do it the old-fashioned way, we’re going to completely blow past our carbon budget. In addition, we’re fighting with the rest of the world on natural resources. China is also using an unprecedented amount they’re doing 50 bridges a year. They built one large dam every day since 1949. They’ve consumed more concrete in two years than the United States in all of the 20th century. It’s just an unprecedented, almost inconceivable amount of just stuff. And at the same time in this country, we love buying crap, right? We keep buying crap and new dollar stores opened today in the United States. Every day in the United States for new dollar stores open that’s $1 store every six hours if you’re paying attention. There are now more dollars thrown around Walmart, McDonald’s, and CBS combined. So a lot of stuff. And so it also explains why we reached rather a grim milestone last year, where for the first time in human history, all of the man-made mass now outweighs all the earth’s biomass. In other words, all of our crap now outweighs all life on Earth. And hence, that’s never happened before. It should be a sign that we’re doing something horribly wrong. It also explains why the richest guy on Earth is the guy that sells us all this stuff, right? That kind of tracks that kind of makes sense. And this is why I’m interested in the circular economy, this idea that whether it’s a tank, whether it’s making, or whether it’s waste, all those can use improvement. And all of those may come in. Alright, so this is, this is how I see us right now basically, we’re just kind of like lottery that we’re just completely oblivious to the world going on right over there. And as a result, this is where we now are, we are at a proverbial fork in the road. The decisions we make in the next 1218 months are going to determine the rest of our century. So do you want to have a bad century or a really, really crappy century that’s basically what’s ahead of us, unfortunately, and I so I almost feel like we’re all just tiny little dinosaurs just completely unaware of you. I think of our heads.

8:15
Jim Collins has he’s got a book called Good to Great I imagine a lot of you have read a wonderful book, but at the end of the book, he does this exercise with CEOs. And he asked them to name the five most brutal facts that are facing and so I did that for the construction industry and brutal fact number one is that climate change is going to change everything. As soon as we put a price on carbon. How we deliver our buildings is going to have to change whether we like it or not. Number two, the most brutal fact is that liability is going to make whole areas un-insurable, and by areas I mean both geographic regions like the southeast portion of the country, but also whole industries just un-insurable. Number three most brutal fact is that concrete and we use a lot of it still does not have a low carbon alternative yet. I mean, there are things that we’re doing I’ll show you some of them but still, it’s it’s not great. Number four, the most brutal fact is that this demand for materials is going to require that we come up with something else. We can’t keep fighting over the last scraps of raw natural materials. We’re gonna have to build engineer living materials, we’re gonna have to use waste in a new way to create new material, we’re gonna have to grow materials. And it’s all TED talk just on that idea, just let’s grow materials. And the fifth most brutal fact is that the construction industry is going to have to deploy technology in a way that’s never done before. And it’s not really quick to do. So. I’m really here to ask all of your help to do that because really, we’ve been building the same way for 200 years. And we’re not really deploying technology in any meaningful way and we’ve shot you out badly we’re not doing this really leads me to six kinds of clear objectives here. Number one is that we have to make every building as efficient as possible, including the existing ones. Number two, we need to target net-zero on every single thing that we do. Number three, we need to use every tool in our toolbox. Because that’s just where we are number four, we need to measure everything measurable, that including includes electrification of buildings, data occupancy, you know everything, and we need to do it now. And finally, we need to use nature as the technology we need to deploy nature at scale. That means by remediation phytoremediation micro remediation, thermal mass passive cooling, passive solar, all the things that nature does for free. We need to deploy. As I mentioned, we’re not using technology and construction really, I mean, I know there’s a lot of companies that say they are they call me every day, but we’re just dabbling compared to what other industries you know, compared to what Google and Apple and Oracle are doing. It’s laughable, so we’re not really mining any data or insights. We’re not really seeing the productivity gains or automation gains that we should be seeing as that other industries see, and we’re not really deploying prefabrication yet, and keep in mind that kind of design. Phones are a prefabrication company so we’re trying we’re getting every word I feel like we’re infants and all this

11:17
wretched stuff you need to get to zero by 2050 Only a bulk of those cuts by 2030. And just if you’re paying attention, 2030 is not 10 years away. It’s light-years away. It’s 90. It’s like 95 months away. So I know it feels like it’s 10 years away, but it’s much closer than we might realize. So there is no time here we have zero years. We’ve been telling our clients that exam but 2014 planner 2015 plan, that that is a two-day plan. There’s no such thing as a 2040 plan, just off whatever you got, and put it into action. And if you don’t have one, well then quite frankly, will help make you one and we’re architects. We’re working on the climate action plans for our clients. One of the things I struggle with is and I struggle with this all the time, is anytime that we’re demolishing a building, anytime that we’re building a double glass box, or anytime that we’re not targeting that zero, and I’m as guilty of it as anybody. Anytime we’re not doing those things. I feel like that is a form of climate denial. We’re denying the reality that’s facing this and I struggle with this as an architect, right? We know better, and yet we still do these things. And that’s kind of the problem. But the one thing that gives me hope is that we created all this destruction by accident. We weren’t trying to destroy the climate. We just did it by accident, to the one thing that gives me hope is imagine what we can accomplish if we try to imagine what we could do if we actually put our heads together and set a vision and so that makes me feel better. Number two is looking at cost in a new way. And this is really something because when you’re in the sustainability world you’re constantly being asked for the ROI for the payback. And I don’t see other industries or other disciplines getting scrutinized this way, right? We build $1.6 billion stadiums that are used eight times a year by people who don’t really can’t really afford it, to go watch a bunch of million. I don’t think it’s about the money. But the same thing with you know, when working in a building, and a client will turn and say, Hey, for sustainability, I’m going to scrutinize every single thing that you do, but where are we with the $26 million parking garage that we’re

13:28
Subsidizing everybody’s parking and driving habits for 26 million? That’s fine. That was on question. But this idea that suddenly for sustainability, we have to show a payback of fewer than five years for everything. That’s kind of nuts. I was in a meeting the other day and one of the engineers, you know, made a joke and this happens a lot in sustainability. You know, he said you know, hey, I eat a salad today. How many elite points is that get us and you know, everybody laughed? But, you know, obviously, the answers to right everybody knows you get to elite points just for eating salad. That’s common practice, but the point is we’re not selling the value right? Nobody ever questioned the ROI and the coffee and donuts in the office. They just do it because they want to do it. Nobody ever questions the ROI on Christmas decorations every year. They do it because it’s needed. You want to do it. So I don’t think it’s about the money. I think this idea of chasing ROI over time is a trap. Really. The truth is that a green building is a better building a green building offers benefits that have value. And so what we do is we don’t sell clients on green building, we sell them on the outcomes, that sustainability brings improved test scores and students better recovery times, lower operational costs, right all of those have outcomes and those have values if you do sustainability, right. It doesn’t cost you money. It makes you money. Because there are orange slices, and then there are orange slices and the two are always the same. The truth is that if we save the planet to keep warming below, what three degrees, which is not a great target, by the way, but even if we just do that bare minimum, we could save $10 trillion by the end of the century potentially dollars is calculated savings that we can do. If you think about the normal health care experience with long waits slow conference high-stress environment, the typical educational experience with bad spaces bad lighting bad ways to think about the typical workplace experience for air for like for connectivity to your neighbors. Oh great, right. But if we start to track these outcomes of the change that we measure, potentially we could change the world. Think about how we normally measure success in a building. It’s what cost per square foot, maybe revenue per square foot, maybe payback and ROI. But how should we measure success in the building? Shouldn’t we also measure patient recovery times stress reduction on the patients in the family’s staff retention all those things have a lot of value? And so let’s calculate those two and how else could we measure success in a building? Can we also measure the amount of energy we produce the amount of carbon that we sequester the number of species we provide habitat to? Yeah, and those things also have value if we change what we measure, we can change the world which leads me finally to number three, our approach to innovation how we do it we bake sustainability in the outset, we have to It’s the biggest change that I made when I came to Canada design. I moved it up in the agenda from the last item to the first real and it needs to show up on every page of your pitch every page of your proposal. One of the other things they did was we have these meetings, everybody sits around a bunch of expensive experts sitting around telling me what they think. Why don’t you know what I really want to hear? I want to hear what they wonder. That’s the good stuff. And just that little trick of saying Don’t tell me what you wonder that lets them into this kind of innovation mindset and suddenly they’re much more open and receptive to things you know, you’ve seen this last few years what happens when people try to understand something complex like science, and it kind of doesn’t work. And so this idea that everybody’s gonna suddenly understand climate change is, man, it’s a joke. It’s not gonna happen. We need to just make it appealing to everybody. Because people are dumb, you know? They’re, like, pretty dumb and you’re preoccupied. They’re like, whatever the hell’s going on with ketchup right now. They’re really busy. So that’s the thing. The thing about innovation is it never looks the way you want it to look. Right? It’s messy. The inventor of the light bulb had to do it by candlelight. So again, it’s not something that you normally ever see. And by the way, I know what I’m talking about because Canada designed the photo, the number two most innovative company in North America. Most agreed on architecture, and by the way, most innovative companies. So this is something that’s in our DNA. And by the way, I know you’re thinking well, who was number one was Chobani yogurt. And I mean they make a nice yogurt, but it’s kind of weird to lease Do you ever come in? So just deal with that, okay, so you have to choose innovation. It can’t. It’s not gonna happen by accident. It’s not going to happen because you put it on an agenda you’re going to do it we like to say that innovation is an avalanche. In other words, every snowflake has to participate in the innovation. And also we tend to focus on clients’ pain points, because the things that are driving them nuts are the things that they’re suddenly going to be much more receptive to switching out. And just lastly, I want to show you this very quickly. What we normally do is we show them their priorities and we haven’t set the dials to where they are we do the same thing with payback ROI. We do the same thing with waste, what they’re spending money to throw away. We deal with risks and liabilities. And so the idea is that we’re mapping out their pain points and getting them where they need to.

18:30
Okay, I think that’s the end of my time. So that being said in the time that I’ve been talking another million tonnes of co2 has been released, it never stops. So we really need to up our game. I’m hoping this has opened your eyes to that challenge. And really, if we can start to take this outcome-based approach to technology and sustainability, wouldn’t that ultimately be the pot of gold at the end of the rainbow? So thank you all very much. Thank you for having me. And hopefully, I stayed within my time reasonably

18:58
Awesome. A No. I completely agree. It has to be innovation is cannot be an afterthought. It has to be actively part. And I wouldn’t, I wouldn’t agree with you that people are not stupid, but they just have so much on their mind so much. And that’s exactly what we say like the things that we are talking about climate change now is like, Oh, it’s too late. You know, we are still under this denial that it’s not happening and we have just right into the fact that we are in this acceptance in that now whether we are in denial or not, it’s happening and that’s why they are on wants to be at the forefront of this will not happen with the data privacy and data. So like you know, a few years later, we are waking up to the fact that we are still in denial. Data. Privacy is not as saying and everything is good. Yes, we need to be proactive. I completely agree. A lot of good questions. I’m going to start with the couple. I know Susanna says that yes, we did. A lot of I did a lot of crazy things during the pandemic but I didn’t think about escaping art or being an astronaut. So yeah, that’s true. An astronaut should have been on the list. Okay. So Taurean Dyer says Thank you, Eric. So many people have been saying that about concrete. However many places can’t seem to figure out a good alternative. What is what are your list of viable alternatives that nearly any country can

20:40
Oh, well, some of my favorite alternatives are just, you know, engineering the buildings. Right? Normally, we just kind of assume the default thicknesses of everything we do out of habit. But if we actually sharpen our pencils a little bit, what we found is that we can reduce it by 20%. Just by doing the maths. Oddly enough The other thing that we’ll do is find alternatives, right? So instead of Portland cement, which is the cheap ingredient, we can use something else. And a lot of those alternatives used to be free. Now their people are wising up and they’re still cheap. So, fly ash, blast furnace slag, there’s a whole bunch of these alternative admixtures No wonder once every time that use of those that are a ton of carbon, a ton of Portland cement you not to use, which is a ton of co2, you’re not emitting so you’re taking waste products and putting into the concrete and, and it works and there’s a lot of documentation out there for it. And then we’ve been playing with companies that do these admixtures like carbon cure, where they inject co2 directly into the water of the hungry mix and become a carbon sink that way, and it’s pretty interesting. So we’ve been playing with a lot of these things. Again, the cost is an issue everywhere, not just in certain places. I mean, our clients are all worried about it too. So we found that you know, efficiency is always the cheapest low hanging fruit. So we always start with that point and then we start to move up to technologies on efficiencies.

22:10
Another use our LinkedIn user access, you know that we are streaming live on LinkedIn, Twitter, and YouTube and we are getting questions from everywhere, which is yeah, it’s everywhere. It’s they’re all coming in. So how do we get and go about creating more sustainable materials and making current buildings’ construction projects sustainable? I know you’ve touched upon this a little bit.

22:34
So there’s, there are two parallel worlds going on right now. There’s a world of waste. And they’re their own thing, and they don’t really talk to other people. But it’s a pretty interesting world because lots of valuable resources moving down this world. That includes agricultural waste, but also all municipal waste all the time and find people finding ways to try to close that with TerraCycle is a good example of a company that thrills scaling that is taking place and making it into the product. The other one is the biology CRISPR crowd that is manipulating DNA to grow things for what they want. And their goal is ultimately to grow a new kidney or grow a new heart or something like that, right where they, they essentially call your heart. That’s basically that’s their ultimate goal. But in the meantime, what we can do is we can grow materials. These materials could have some of the qualities and characteristics that we want. So I could grow a panel that could heal, or I could grow a panel that could absorb co2, or gives up oxygen. I could grow it I know that glows in the dark, really we already know all of the genetic codes are all those little features. And it’s just a matter of programming and so one of the things that we’ve been working on for the last, however long is this idea of how do we do that? And there’s a number of companies now that we’re kind of working with.

24:09
To help them on to do that. One of my favorites is Bio-based. Their ultimate goal is to grow concrete. And so they’re taking recycled aggregates, basically stone dust, and mixing with bacteria. And they’re growing concrete tiles, you’re not really creating something else. It’s but it’s pretty wild. And I’m very excited to see where they go with it.

24:33
Thank you, Eric. I think that’s all the time we have more questions from our audience. So stick around on the chat, the comments section of the live stream. And if you want to answer a few of them take this question that will be great. It was a pleasure having you and thank you for your time.

24:50
Oh, thank you all good. Seeing you all and hopefully next person. Oh, yeah.

24:58
Thank you.

00:00 Shilpi Agarwal:

AI Diet stands for Data and Diversity, Inclusion and Impact, Ethics and Equity, Teams and Technology. Let’s build a better AI world together that is cognitively diverse and inclusive in every way, and with that I would like to welcome our first guest Christina Montgomery. Christina Montgomery is IBM’s chief privacy officer and an IBM vice president as chief privacy officer, Christina oversees IBM’s privacy program, compliance and strategy on a global basis and directs all aspects of IBM’s privacy policies. She also co-chairs IBM’s AI Ethics board a multi-disciplinary team, responsible for the governance and decision making process for AI ethics policies and practices. Prior to her current role, Christina provided legal support for many of IBM’s divisions including spending more than four years as secretary to IBM’s board of directors. Ms Montgomery received a BA from Bingham University and a degree from Howard Law School, ladies and gentlemen please welcome our keynote speaker for AI Diet World 2021, Christina Montgomery. And with that let me welcome Christina to the stream, hey Christina welcome!

Christina Montgomery: Hi Shilpi, thanks so much for having me today, our pleasure and thank you for being here. I know my team said that they couldn’t hear me was the intro everyone could hear it I heard you fine all right awe me!

Shilpi Agarwal: I’m going to go backstage and the stage is all yours, Christina welcome.

Christina Montgomery: Great, thank you, yeah thanks Shilpi and thanks for everybody who took the time out of your day to join us today uh today I’m going to talk to you about the importance of trust and why trust is important with respect to AI.

If you’re attending this event you are well aware of how pervasive AI is becoming in our daily lives it’s spread across all industries it’s become embedded in everyday business, in fact three out of four businesses are exploring or implementing AI and there’s a significant amount of investment that businesses are putting into AI. Key reason for the increasing use of AI is this growing amount of data, the massive amounts of it structured and unstructured, that’s being generated from businesses and individuals every day from an expanding set of applications like websites and mobile apps and devices like wearables or appliances, connected cars, manufacturing equipment, cameras, the list goes on.

“On a recent survey found that most businesses use more than 20 different data sources to inform their AI and large businesses sometimes tap into 500 or more data sources”

Christina Montgomery: Considering all of this information and the means to connect process and uncover insights from it it’s easy to see that AI is changing the way we do business, but the effect of AI goes beyond business applications.

“AI is opening the door to create innovative solutions with the potential to solve some of society’s biggest challenges”

Christina Montgomery:  For example, using AI to analyze data researchers at IBM and the Michael j fox foundation developed an AI model that predicts the progression of symptoms in Parkinson’s patients in terms of both the timing and the severity Magnavox, a non-profit based in Paris built a platform with IBM that uses AI to scour information from trusted sources to monitor the world’s forests and the rate of deforestation and to act on those insights before further damage is done and finally

“Working with the United Nations environment program in the Wilson center,  IBM has demonstrated how digital technologies can help to monitor marine pollution and fundamentally change how data analysis and insight are accessed to enable more informed decision making”

Christina Montgomery: AI enables organizations to implement global strategies around big picture issues, for example, IBM’s newly released environmental intelligence suite allows organizations to plan for and respond to disruptive events like weather to avoid outages and ensure business continuity in real-time with their operations around the world it’s powered by AI that integrates weather data with business operations that augment decision making with enterprise processes to address sustainability resiliency and climate change challenges innovative solutions like this demonstrate that new technologies like AI can play a critical role in helping address important issues in today’s world but AI presents me unique challenges.

Christina Montgomery: First AI, as we mentioned is made possible by vast amounts of data collected every day but often there’s a lack of transparency around data collection it can be unclear to consumers who have access to their data and how it’s being used and protected, even what data is being collected. Second there’s a pacing problem around new technologies like AI,

“In some cases new technologies are being created and put into use quickly that regulation can’t keep up that leaves it to companies and governments to impose their own guard rails around technology development deployment”

Christina Montgomery: And use the combination of these factors presents the very real risk of eroding public trust around technology and if Society doesn’t trust the technology we won’t see it being used and deployed stifling innovation. For these reasons it’s essential that we lay a foundation today that ensures new technologies put people first and that their benefits are felt broadly across society.

Christina Montgomery:  At IBM we align ourselves to what we call our principles for trust and transparency at a high level. These are that AI should have meant not replacing human intelligence, that data and the insights generated from that data belong to their creator in our case, that’s our clients and that new technologies including AI should be transparent and explainable.

We impose firm guard rails on our own use of technology and data and we advocate for others to do the same through concrete actionable policy recommendations, having a set of principles is one thing, though the real trick is operationalizing them and you have to work to infuse them in your practices across the company.

Christina Montgomery: Essentially your values must be your practices. We align our practices around the principles I mentioned earlier in a set of pillars around AI ethics. The fundamental properties we view and are required for trustworthy AI are the pillars, which provide an intentional framework for establishing an ethical foundation for building and using AI systems and they’ll be familiar to many of you.

“There are essentially they are explainability, fairness robustness transparency and privacy and the principles and the pillars together demonstrate IBM’s long-standing commitment to taking an ethically conscious approach when bringing new technologies into the world”

And we embed ethical thinking throughout IBM via the AI ethics board which I co-chair along with the IBM AI ethics global leader Francesca Rossi IBM’s AI ethics board infuses the company’s principles and ethical thinking into business and product decision making. It provides centralized governance and support and accountability while still being flexible enough to support decentralized initiatives across our global operations,

“And that’s no small thing when you remember we have about 340 000 employees operating in 170 countries plus around the globe”

 

Christina Montgomery: The board provides too small and two-way engagement promotes best practices conducts internal education and leads our participation with stakeholder groups worldwide but in addition to the board we have a network of AI focal points and these are embedded throughout the business the focal points help ensure that our approach to AI ethics is both tops down and bottoms up instilling a culture of trustworthy AI throughout IBM the board and its associated governance are just one of the ways that we embed trust in our operations.

“At IBM they’re a mechanism by which we hold our company and all IBM’ers accountable to our values and our commitments to the ethical development and deployment of technology but no company can do this alone”

Christina Montgomery: We advocate for policies that are consistent with those values we’ve been very public in our positions for example to firmly oppose the use of technologies including facial recognition technology for mass surveillance racial profiling and violation of basic human rights and freedoms in fact really any purpose that’s inconsistent with the principles of trust and transparency that I mentioned earlier when our CEO Arvind Krishna announced that we would no longer offer general-purpose facial recognition

Christina Montgomery: Software products we were the first major company tech company to do so and others soon followed we also partner with leading minds around the world to advance important ethical dialogues. The efforts we’ve seen at a global level from the EU high level expert group on AI to the Vatican’s Rome call for AI ethics are laying a clear path for AI that’s deployed responsibly and ethically and they’re all efforts where IBM has played a leading role.

“Last year we partnered with the university of Notre Dame to establish the Notre Dame IBM Tech ethics lab it’s a first of a kind cross-disciplinary research initiative focused entirely on driving best practices in technology and AI ethics and in shaping relevant public policy”

Christina Montgomery: Our CEO is co-chairing the world economic forum’s global AI action alliance, a platform that will focus on moving beyond principles – one that will convene multi-stakeholder dialogues, engage in real-time learning and help scale best practices all in support of trustworthy AI.

As part of that alliance we’ve committed to sharing our governance approach to AI fix those concrete steps that we take as a company and we’re hoping that that will help guide best practices in the space as well internally. We’ve launched tool kits like AI fairness 360 to help customers and other organizations check for and mitigate bias and AI systems and AI explainability 360,  to help explain a system’s decision-making process.

Most recently, IBM research unveiled a toolkit called uncertainty quantification 360. That helps developers and data scientists quantify the uncertainty of machine learning predictions with the goal of improving transparency and trust in AI,

“And recognizing that the challenges of AI bias and explainability are bigger than any one organization can tackle independently.”

 

Christina Montgomery: We contributed these tools to the open-source community that anyone can use them and improve upon them along, with working with outside organizations and providing Open Source tools. As a company we advocate for responsible technology policies with policymakers around the world, in addition to our position on facial recognition we’ve called publicly for governments to strengthen trust in technology in a number of areas, for example, we support a strong and bipartisan national consumer privacy law in the United States, not a patchwork of different rules at each state-level.

 

“Privacy prediction should vary based on where someone lives or from where you access the internet in the United States. The framework should be flexible enough for organizations to implement while driving accountability.”

 

 

Christina Montgomery: I’m Sorry I’m seeing someone in the chat, can people hear? Shilpi should I stop or?

Shilpi Agarwal: Your fine Christina people can hear, great.

Christina Montgomery: Sorry I saw the text traffic on the AI front we’ve called for precision regulation of AI, so this is you know essentially a call to place the tightest regulatory and policy controls on technology end-users where the risk of societal harm is the greatest,

“In other words, regulate the use of AI not the technology itself and in addition we’re calling on governments to help strengthen the adoption of the testing assessment and mitigation strategies to minimize instances of bias in AI systems”

Christina Montgomery: New laws, regulatory frameworks and guidance for mitigating bias and AI systems are on the horizon and if well-crafted these measures can provide industry and organizations with clear testing assessment mitigation and education requirements to enhance consumer confidence and trust in AI.

“I firmly believe that we can change the world with technology but we have an obligation to do so responsibly.”

Christina Montgomery: We take that obligation seriously at IBM and we’ve been a responsible steward of new technologies for more than 100 years with the rapid pace of technology development, it’s even more important that we hold firm to our values embed them in our practices and advocate for policies that align with them. We consider trust our license to operate and it’s incumbent upon us to continue embedding trust in all we do, as we create and use these technologies to build a better and more prosperous future.

Christina Montgomery: Thanks for your time today and I believe we have a little bit time for uh for Questions.

Shilpi Agarwal: Thank you, Christina, great talk, yes we do have a few questions coming in the audience we are ready for your questions

 

“Christina, I love that you said that yes with great power comes greater responsibility and at IBM you take this really seriously and I loved all the examples you shared”

 

So if a company was like you know putting these AI governances in a place where would you tell them how do they start where do they start?

Christina Montgomery: Yeah I mean I think it’s a little bit different for every company and I that’s the challenge that people have right now is to figure out how best to ensure leveraging the current business structure that they have in place right to start thinking about these considerations in much the same way I know, from our perspective we think of it as a few years ago we were looking at concepts like privacy and security by design

Christina Montgomery: So we think about it as ethics by design from that perspective and just building upon the existing practices we have in place and ensuring that we’re embedding in the day-to-day decision-making that perspective I also think you need to start with your foundational values and then use the unique uh organizational construct in your own business build upon and embed those values uh in the most effective way in your business yeah that’s true so what is the best way to identify um bias um is it possible to even identify bias at the data stage and at the algorithm stage or is it only after the model has been deployed can you actually figure out that there is a bias in the system oh no I think it’s throughout the life cycle that’s one of the things that’s sort of unique about AI is you start with.

Christina Montgomery: Something and what you end with once you’ve deployed it is often very different because the model learns over time and trains on new data and the data evolves, so we advocate for a life cycle approach to things like bias starting from the very concept when you think about what is the purpose of the end purpose of what you’re trying to accomplish and what do you need to train on that and looking at data sets again from the beginning throughout the life cycle of the product.

Shilpi Agarwal: Yeah we have another audience question, at the lowest level how is this implemented if someone is at the deploy development stage of a product how does it go from top down to bottom or is there any other way around it?

Christina Montgomery: Well I think some of the things um that we do here at IBM is this sort of ethics by design concept, so starting I think it starts from the very beginning when you’re thinking about, as I mentioned what are you trying to accomplish what data do you so need to accomplish that just having people start thinking about being aware of these issues here are the questions that you should ask here’s what you should check for in your data here’s what data you need to accomplish the end goal.

It is sort of starting with a vision and end vision but you know backing up and looking at to accomplish that what are the questions, I need to be asking at the beginning

Shilpi Agarwal: yeah, very true um we wow we have a lot of questions.

So Alberto asks what about Clearview AI and Amazon that still self, facial recognition, AI yeah I mean I you know I mentioned in my remarks that there’s often this pacing problem where regulation can’t keep up and it’s kind of up to companies to set their own guard rails.

I can speak for what we advocate for you know we don’t um sell general-purpose facial recognition anymore because we don’t think the technology is ready we think there need to be uh there needs to be a closer look at the use of facial recognition particularly in the context of law enforcement but other uses as well.

 

“That’s our point of view and we’ve called on regulators to start taking a look at that right now, you know those come what the companies are doing in that space and Clearview, in particular,  there’s no law essentially you know stopping it”

 

Shilpi Agarwal: So it’s the guardrails that companies need to put in place to build trust – yeah absolutely.

Bruce says you mentioned a call to regulators to focus on the specific uses of AI, can you speak more to the ask for regulation and what it would mean to us developing AI products?

Christina Montgomery: yeah, what we called for in our policy please we have by the way for those who are interested the IBM policy lab offers, as I mentioned concrete actionable policy recommendations – we have a number of papers in that lab.

You can read in more detail what we’ve suggested in things like precision regulation for AI, or regulation for facial recognition technologies – but it’s for regulators to initiate what we’ve called for is an initial risk assessment.

“If you look at something very simple like facial recognition or something everybody will understand right you use it on your smartphone um its one-to-one match the picture on your phone doesn’t leave the phone.”

 

Christina Montgomery: So no one else has access to it,  you can choose whether or not to implement and if you use it on your phone it’s just easy it makes it easy for you to unlock it but facial recognition used by police force and cameras on the street very wholly different use of that technology and what we’re suggesting it’s not the technology, it’s not the ability to recognize a face, it’s the actual application of how you’re going to use that particular technology that regulators should look at.

That’s at a very high level, the distinction between the technology itself and the use of that technology.

Shilpi Agarwal: sure thank you, Christina  – Michael McNair,  in the education sector has a question – I know we are a little over time but these are some great audience questions, do you have a few more minutes Christina? Thank you.

In the education sector what types of people should be a part of the data governance committee and AI committee,  should we start with consulting first I mean I I’m not working within the education sector myself?

 

Christina Montgomery: So it’s kind of hard for me to answer, but I would say just as a general premise, when you’re thinking about we’ve been very much active in advocating for multi-stakeholder dialogues right?

 

“So if you’re talking about the appropriate use of technology, data, like having a diverse cross-disciplinary lot of people from different backgrounds”

Having stakeholders as well those who will be subject to regulation to those who will be the subject of an AI application, those who work on it from vastly different backgrounds diversity is really critical because you get lots of different perspectives that you might not otherwise experience or take into account.

 

Christina Montgomery: I think from our own ethics board for example we have a very cross-disciplinary board we have people representing pretty much every business function within IBM lots of different backgrounds some are technical some are not technical and we feel that that really brings us lots of different questions that you would you know one pillar alone isn’t going to have the answers 

Shilpi Agarwal: So that diversity of background is really important and we’ll just take one more question, the last question for Christina – thank your audience for the questions and thank you, Christina, for spending some extra minutes with us here today, Alberto Roldan asks what’s the quantitative measure of trust and transparency?

Christina Montgomery: I don’t know if I can answer quantitative these questions, yeah I mean look every day we ask questions about what we’re doing internally and the like I don’t think there’s anything mathematical a lot of these are really hard questions which is why I feel it’s really important to firmly adhere to some north star for us that north star is the principles right?

So you take a hard line about things but often there’s you know, it’s often you’re in a grey space right, where you have to make a call and being able to point to those principles has been really something that’s been very valuable for us, but quantitative I don’t think there is one trust, this is really what it comes down to in many respects right?

“You’ll be able to see if technology is entrusted if people don’t want to use it,  if businesses can’t use it because their customers won’t trust it, that’s where the measure is. That’s one way to measure it true.”

Shilpi Agarwal: all right that’s all the time we have for Christina today thank you so much for being with us uh fabulous talk and thanks, thanks to Kevin and Aaron also for you know helping us coordinate all this and getting us Christina thanks again Krishna take care bye-bye thank you bye.