Talk Summary

In this #3 episode of the DataEthics4Allᵀᴹ Ethics 1stᵀᴹ Live Talk, the DataEthics4Allᵀᴹ Leadership Team will discuss our newly launched STEAM in AI Program during the National STEM/ STEAM Week. STEM has traditionally always been about Science, Technology, Engineering, and Mathematics.

Then they realized that Arts was an important part of this equation and so came STEAM. DataEthics4Allᵀᴹ believes that in order to break down barriers of entry in Tech through a grassroots approach, we need to include nonlinear paths such as Sociology, Tutoring, Ethics, Analytics, and Mentoring that lead to careers in Artificial Intelligence besides the linear paths such as Science, Tech, Engineering, Arts and Maths.

Artificial Intelligence is here to stay. It’s being used in all walks of our lives today and every Industry in the future is going to adopt it. So, how can we better equip our next generation with thought leadership, ethics by design, career mentoring and guide them to these new linear and nonlinear STEAM pathways that lead to Artificial Intelligence? Come, Join Us in the STEAM in AIᵀᴹ Movement!!

 

 

1:28 Shilpi:  Hello everyone, and welcome to this episode of Ethics 1st live talks, with food for thought with ethics first leaders.

2:48 Shilpi:  I love this new series of conversations that we have started called Ethics 1st. It’s very near and dear to my heart because of two reasons. One, the things that we used to talk about in our leadership meetings and the things that we generally talk about with our friends on a regular basis, we always have so much to say, everyone relates it to the current affairs, and the things that are going on around us and how we put ethics first in things that are important to us, that’s a conversation of everyday life now for us, so to be able to bring that to a global stage, and to be able to bring audience comments and participate in all of this, and understand what it is that is shaping our lives today, and we are bringing food for thought for people who are ethics first minded, who are thinking leaders who are trying to bring change in their organisations by being ethics first. That’s why this is so important and fun. I think this is a very important discussion that we have started. And so who can tell me about what we are going to talk about today?

4:15 Sam:  That’s a great question.

 

4:18 Sam:  Yeah, this evening I think the main aim is to discuss our new DataEthics4All Foundation ‘STEAM in AI’ program.

4:33 Sam:  Is it STEM day today as well that we are celebrating?

4:37 Shilpi:  November 8th was the actual STEM day, Susannah do you want to add something to that before I explain more about STEM? 

4:54 Susanna:  Yeah, STEM Day was this week so we are actually celebrating it for about a week.

5:01 Susanna:  Shilpi has a great announcement to make.

5:04 Shilpi:  [laughing]

5:05 Shilpi:  Not just Shilpi, our whole team, we are the team, right? We brought this programme up, we came up with this cool name together.

5:14 Shilpi:  There was just so much brainstorming behind the scenes for everyone who doesn’t know, there’s always so much discussion going on. Before we announce something, we put a good amount of thought into it just like anyone else. But yes, we do that as well, a lot of brainstorming on what acronym should we choose that really stands for what we want to do, our vision for youth, and how we want to change the world of AI together with you, and also for our next generation, so we gave it a lot of thought. Because we have some very unique things that we offer, like career mentoring, career technical guidance, STEM tutoring, we wanted to find an acronym that befits everything that we are doing, and it was, believe me, it was so hard to come up with one.

6:12 Shilpi:  If you look at our drawing board and everything that led us to choose the name ‘STEAM in AI’, it’s been quite a journey.

6:29 Shilpi:  Like Susanna said, National STEM Day was November 8th, but we are celebrating a whole STEM week. And if it was up to us, we would celebrate a whole year and a whole decade, right? That’s what we stand for. We really feel that we are bringing the STEAM into AI. So who can tell me about what STEM stands for?

7:01 Sam:  Shall I go first?

7:01 Shilpi:  Yes, the traditional meaning, not our version of it. 

7:01 Sam:  Yeah, yeah.

7:10 Sam:  Yeah. So, in the UK and internationally, we use the acronym ‘STEM’, and I think what often we refer to are the quite heavy and technical science-based subjects, so the sciences; biology, chemistry, physics, and then we’re looking at mathematics. So technology is ‘T’, and then we have ‘E’ which refers to engineering, and mathematics clearly. I think they’re the ones we look at, yeah.

7:44 Shilpi:  Absolutely. And then Susanna, how did we get to STEAM, do you know?

7:52 Susanna:  STEAM? From the traditional word, they added one more letter for arts, to try to bring in arts. Arts meaning, not just arts like music and painting and drawing, but arts as well as the humanities, were all brought into that one letter. Yes, so that is STEAM. 

8:12 Shilpi:  And now national STEM/STEAM day is a combined day to celebrate STEM and STEAM. Because it’s an important revelation that the arts are also important, humanities is a bigger umbrella, arts is an important subject to include in STEM, and the DataEthics4All leadership team, we just can’t stop at that right? So we had to come up with our own version of STEAM, and we think, if you look at everything that we have done so far, and what we stand for, what we are trying to do is create linear as well as nonlinear STEM career pathways for our youth, especially leading into STEM and also artificial intelligence. And so that’s why we decided to include many, many more subjects and areas, some things that are unique to what we offer. So who wants to talk about our definition of STEAM?

9:19 Susanna:  So Sam, you go first and then I will go.

9:22 Shilpi:  It’s like a quiz, like a look at this, my own team!

9:28 Susanna:  We may fail this quiz!

9:34 Shilpi:  Just add the ones that we have added, so for S, we have added what?

9:40 Susanna:  Sociology.

9:40 Sam:  Yeah, that’s right. That’s it. That’s it.

9:45 Shilpi:  Susanna, who can better tell us about sociology than you? So let’s start.

 

 

9:50 Shilpi:  So today our whole topic is going to be about the three of us who have come from very different backgrounds, and how we have all come together and how we are helping shape the next version of AI, or trying to build the better version of AI.

10:08 Shilpi:  We come from various very different backgrounds, which will be the topic of discussion today. And we’ll start with Susanna and ‘S’ which is ‘sociology’, and so she will tell us how we added sociology and a little bit about her background.

10:25 Susanna:  Well, I know how we added ‘S’, you know, we just kept on brainstorming what to add for the letter ‘S’, it was social sciences, sociology, and there are so many things that we could add but the social sciences itself is one of the key areas for artificial intelligence in my opinion.

 

 

10:44 Susanna:  Because artificial intelligence is built on human data and you can’t take sociology out of human data, there is no way you can do that, and if you want to build an AI system that is going to be ethical and is going to be beneficial to humanity, it has to have sociology in it.

 

11:03 Susanna:  My background is from social sciences, psychology is an interdisciplinary field of social sciences. So I study human behaviour, and I study how human behaviour can be interpreted, human emotions and human behaviour, human cognition which can be interpreted in a way that’s understandable by machines. So that’s where my background is. So yes, I’m glad that we incorporated sociology into the STEAM. 

11:34 Shilpi:  Yeah, because just like you said, psychology, it starts with P, but yes, all these sciences, right? Even on social media, we have had town hall discussions on how social media is affecting disinformation and polarisation, we have had our youth, as well as our [inaudible], participate in this discussion.

11:55 Shilpi:  So today, you can’t leave the equation of sociology out of the equation of artificial intelligence, right. So it is a very important part. And that’s why we have decided to include it.

 

12:11 Shilpi:  Okay, so let’s move on to ‘T’. What is it that we added to ‘T’, our version of the ‘T’?

12:18 Sam:  Yeah, we had technology in the initial STEM revision, but then we decided to add technical education, and I think that really encompasses a lot of subjects within it. It’s not always the pure computing programmes or computer science programmes, but it does include that. Yeah, it could be studying, for example, how to build applications or web design within an applied programme. And it could be, for example, understanding natural language processing, to build applications in NLP, which is where I’ve got my experience. I’m a linguist, and I started out as a languages specialist, with a technical background in economics and business, and I developed language programmes, but I also started moving into natural language processing because I know that, as Susanna was saying, there’s a really close link between the human interactions that we have, the human conversations that we have, and the benefits technology can bring to that, and just how much more enhanced our lives can be through technology and language – and yeah, NLP applications like chatbot tools, I have delivered a couple of courses on that recently with our community at the Bootcamp and also with the demo. First of all, I think that – for me –  technical education, understanding the tools and applications, the language and building solutions, which is what we do, you know, which is our goal.

14:03 Shilpi:  The ‘no code AI’, that’s also technical education, even though it doesn’t really involve coding right? The ‘no code AI’ movement is that it doesn’t require coding, but yet it is considered technical education. And so tutoring and technical education are the things that we have added to technology for ‘T’. What have we added for E? 

14:31 Susanna:  [laughing] Ethics, I think?

14:34 Shilpi:  Oh my goodness, this is a joke?

14:43 Susanna:  It is a joke, only I mean, of course, it is ethics. The other one stands for engineering, but we wanted to have ethics in engineering so it’s not a word that we can forget because it’s flashing across the screen of all sorts.

14:58 Shilpi:  With Ethics 1st, our talk is ethics first. Everything we do is ethics first, we want to raise the next generation of ethics first leaders, our AI DIET World celebrates ethics first champions, whether it’s people, products, or companies’ solutions. So we are big into ethics in everything we do, and we want to bring all conversations centred around ethics, no matter what career you choose in life, no matter what role you are, no matter what position you start as, whether it’s that you’re just starting out your career, or whether you are an experienced professional, whether you’re a marketer, or an engineer, or a salesperson, or anyone in any role, ethics should be the centre and focus of everything we do today, right?

 

15:51 Shilpi:  I mean, that’s the only way, ethics has to be ingrained into everything we do by design, only then can we be successful.

16:00 Shilpi:  Because as we all have always said, right, especially at DataEthics4All that compliance is a very low bar, and if we just do the bare minimum, if companies are doing just the bare minimum of being compliant, then ethics is going to be left behind. And if we want to bring ethics at the forefront then we have to go way above compliance, and we have to make sure that, yes, ethics can also be beneficial to your bottom line, even if, you know, it may feel like in the short run, and we had some great discussions around this in our AI DIET World with some great speakers, enterprise speakers where, you know, we talked about how we could really improve the bottom line, not just affect, but actually improve them, because when we are trustworthy and we are transparent and we are following good data practices then our customers trust us, and they value their privacy. So their trust, earning their trust means by following ethics we are going to help our bottom line. So we want to make sure that our kids, our next generation also understands this and put ethics at the forefront of everything they do. Okay, so moving on to ‘A’, what have we added for ‘A’?

17:30 Sam:  We have the arts, we’ve actually added the arts to this as well I believe, and also analytics.

17:41 Shilpi:  So we have added analytics. Yes, yes. Okay.

17:48 Susanna:  Of course analytics and advertising, analytics and data collection. I mean, that’s a big, big deal. 

17:54 Shilpi:  Of course, a big, big deal. Yes.

17:58 Sam:  The insights that we gain from data are profoundly important to businesses and to a lot of organisations. Our focus as well is really making sure that ethics and data ethics is at the centre of that, right, that it reflects our society and biases not brought into the datasets themselves, and understanding when that can happen. Yeah. Yeah.

18:31 Shilpi:  Yeah, go ahead, Sam. 

18:33 Sam:  I’m fine. Go ahead. Susanna, sorry.

18:36 Susanna:  Yeah, definitely, or whatever, you know, I do agree with whatever Sam has said, you know, analytics is a big deal now.

 

18:43 Susanna:  Yes, especially without ethics, analytics is going down the wrong path, and it has been on the wrong path for a long, long time.

 

18:52 Susanna:  So now, bringing analytics and bringing ethics within the STEAM under this broader umbrella, I think we can actually have analytical systems developed that are more ethical in the coming generations.

19:05 Shilpi:  Yeah. And how I see it is that nowadays, data is in so much abundance, right? So how do you make sense out of that data? So for that, we have all the analytics in the world, right? That is the only way we can make sense of the data. And there are just so many tools, wonderful tools that will give you wonderful dashboards, data dashboards, and they will give you opportunities to analyse your data, understand your data, make business sense out of it, whether it’s for finance or for e-commerce or for healthcare, analytics is important to understand different trends, and understand the customers and what they need, and their behaviour and the pattern. So pattern recognition. Everything that we can do through pattern recognition is what we can do through analytics, and analytics now has become, like data analytics and data science have become careers – major careers in themselves, like people, can become data scientists and choose that as a career. And analytics is a big part of that, and analytics is here to stay. I mean, there’s just so much, not just an abundance of data, but the abundance of tools as well, there are 1000s of analytics tools out there.

Shilpi: So you still need a human to be able to understand and make sense out of those analytical tools and the data and the graphs, and to translate that into a business strategy, and to make sure that we are able to utilise that information in a way that it improves our bottom line, right, so only data analysts, and you don’t even have to be the best at math to be able to understand if you have business acumen, and all these data tools will do the job of bringing the results to you, if you feed in the data, they’ll bring the results, then all you have to do is be able to understand that through your business acumen, right? You don’t really have to have the technical coding skills to be able to understand that. So that is again a very, I would say not a direct linear path to data science or STEM like but basically, somebody who is not from a STEM major could also still be a very good analyst. So yeah, that’s a great, great area that we have added to our STEAM. And then moving to ‘M’. Do you want to say what we have added for ‘M’?

19:17 Shilpi:  Certainly, it’s at the core of everything that we do here at DataEthics4All at the core of every grassroots organisation, that is mentoring. So then we have added mentoring to mathematics. So not only mentoring in mathematics, mentoring in all of the letters preceding them.

22:06 Shilpi:  Yes, yes, definitely.

 

22:09 Shilpi:  I think one of the biggest differentiators that DataEthics4All has to offer is that we have a 1000 plus global community of leaders who are from 54 different countries in the world. And that’s not a small feat.

 

22:27 Shilpi:  If you Google today, how many total countries are there? There are like 200 countries, right? So if we have 197, or something like that, so 1/4 representation of all the countries of the world talking about real diversity, inclusion, and also our age groups, right? Who can tell about the different age groups that are part of our community?

22:55 Sam:  Yeah, if we look at the statistics and obviously our data on our website, our youth team are high school age, so we have young students and teams that work together as part of the AI youth council, and then we have experienced professionals that are working in organisations and in healthcare and in various different sectors, I.T. construction, and various different areas, and then we have those that have also finished their careers full time and now are a little bit older and have that experience and that time to give to young people and to the next generation for mentoring. We have an abundance of experience across different sectors and that’s the wealth, the knowledge, you know, that we have as an organisation. It’s really exciting. Right up to 70 plus I believe, or 80.

23:54 Shilpi:  Middle school, we encourage students from grade six to professional age and retired as well right and we all have so much wealth of information. Even young students, I’m amazed at when they come and share or when they come to our town halls or events, mixers, tech mixers, they have taught pet for youth studio courses our AI youth council, they have a clear perspective on how they look at the society and the world today, and they have an opinion about things about almost everything today. So it’s nice to be able to understand and hear their values and where they come from what is important to them. Susanna, what would you like to add to this?

24:55 Susanna:  I believe the age difference is from 8 to 80 or something like that? That’s how wide and how varied we are in terms of this community, it’s diverse! Yeah. It’s also the disciplines, the countries, it’s also, you know, not only just that, even within our team, we have so much diversity. Even though it may appear on the front that Shilpi and I are both, that our native land is both from India, but there is nothing common between us. Other than that, you know, her background and my background are so different, and then we have Sam here who is from the UK, then we have somebody from Canada, then we have another person who was, you know, completely from a very different industry, automotive industry who has now jumped into AI ethics. So we have so much diversity even within our team as well. So that’s something I wanted to add.

25:53 Sam:  Yeah, we are definitely.

25:56 Shilpi:  That is so true. And that’s why what we are trying to say is that. Let me talk a little bit about my background. I started from a STEM career because I am an engineer, trained engineer by profession, a computer scientist, computer engineer, and then I did my Master’s in design and visual communication, and then I practised marketing and graphic design and visual communication for almost 20 years. And I have seen how marketing itself has changed over the years. When I first started in this area of marketing, it was more about creating brand awareness, and everything was like, ‘okay we build websites, and they will come, right?’ There was not that much analytics in place, it was just, it’s one of those things, you have to do it. It’s good to have social media, you have to be there on the sidelines, there was no analytics, no ROI, nothing like that. You spend money on it, and that’s okay. You have the budget, you have to do it, that’s it. I’ve seen over the years how this trend has totally shifted. So the graphic designer part of me liked that it was for brand awareness, but the technical side of me always felt like, how am I doing? If I posted three times on Twitter, how am I doing? Like, am I bringing value to my customers? What does this mean? I spent 50,000, or whatever that money is on ads. So what did we do? What did we get out of it? There was always this question, but it seems like not many people were asking that question. 

27: 42 Sam:  It’s interesting you say that because I think that over the past few years, and from my experience before going into education in my professional experience, there is more demand now in the analytics space for ROI data and essentially, your return on investment on your social media investment. Yeah, and there’s lots of metrics and analytics in that space that is really powerful now, and we didn’t have that before. And yeah, you’re right, it’s certainly an area of growth and an area that we are interested in as an organisation.

28:18 Shilpi:  Yeah so I think it came full circle because with the creative side of me and the analytic side of me, then it made sense

So it came, because of my engineering background, all the analytics and marketing came naturally to me

Shilpi: and people who are purely from just the design side still had some learning in that area. Like how do you adopt technology to become like, otherwise savvy, right? So it has moved in that direction.

28:46 Shilpi:  So I started with STEM, went into non-STEM and I think it’s a full circle now, and then by starting DataEthics4All I saw problems in marketing data.

28:56 Shilpi:  Then I thought we need to do something because marketing heavily uses data, as you know, from collecting data from website traffic, users and visitors, to customers actually leaving the data and then targeting them with ad targeting and then everything we saw with Cambridge Analytica, and then we heavily used ad campaigns as weapons, we did ad weaponisation during the political elections. So, I mean, all of that was very troubling to see as a marketer, that we are not using the data well, in fact, we are misusing data to a point where we are using it as a weapon, and that was the beginning of why I started DataEthics4All and how we moved into AI. So I think I have come full circle in terms of STEM, non STEM, STEM, non STEM and the whole thing.

29:52 Shilpi:  So, I think what I’m trying to say here is that there are many, many paths to STEM and many paths to AI.

30:01 Shilpi:  Some will start off with even non STEM paths just like Susanna, and even Sam mentioned, NLP. sociology, no code AI,  marketing field, they don’t have anything to do per se with STEM directly and yet you can come to AI or you can come to data, you can come to analytics, you can have all those careers, but the pathways will be different for everyone.

30:33 Sam:  Yeah, definitely, and we reach milestones in our careers, and we want to move into different industries and different roles and we have that option now. The technology is there, the applications that we use to build tools are there for us to use and to develop, it’s really exciting. We just have to, as we said, make sure that we think about ethics woven into the design and the fabric of these tools. And that’s what we want to focus on. That’s our mission, part of our mission.

31:06 Shilpi:  And especially for our next generation. All right. So yeah, Sam?

31:19 Sam:  Yeah, no, thank you. That was really, I really enjoyed that discussion.

31:22 Shilpi:  Thank you. I know, we are all so passionate, I can see the passion.

Sam is a passionate teacher and she not only teaches on an everyday basis by profession, but then in her spare time, she volunteers for DataEthics4All, and she teaches workshops and courses, and she learns herself – she’s a linguistic teacher, but she learns technology and she learns NLP and no code AI, and hats off to her for taking on all these things and stepping outside of her comfort zone, and trying to learn all these new things, and then making sure that she’s ready to even teach others, you know, she’s just picked it up, and now she’s ready to teach others, that is just so awesome Sam.

Shilpi: Susanna also has taught courses on education platforms as well as other places and like we discussed in the last episode,  she calls herself and self identifies as an introvert, so it is difficult for people to step outside their comfort zone, for everyone, but more so if you consider yourself an introvert, I’m sure it must be a little harder for her to do that. And yet she has managed to find the courage to put herself out there in front of people for the benefit of society, and join and be a leader at DataEthics4All for all and be here on the Ethics 1st live weekly talks. So I’m just so glad that we are able to create these pathways for our students and for our younger generation and be these, hopefully, be good role models for them. For them to see that anything is possible.

33:16 Sam:  Absolutely, awesome. Yeah, definitely.

33:18 Shilpi:  All right. Thank you both for joining me today, and Happy National STEM/STEAM day or week everyone, to all our listeners, and we’ll catch you next week, same day, same time, take care!

33:33 Susanna:  Thank you for joining us!

33:38 Shilpi:  Please join us, please scan the QR code and join us, join our community, just like we shared a little bit, it has a wealth of information, many, many resources for you, we have the on-demand data ethics institute and the courses, we have the community mentoring and guidance. 

33:58 Susanna:  Yes [laughing]

34:08 Sam:  Have a good weekend. Bye bye-bye.