There are a lot of Dangers to Human Society if Data Science is used without Ethical Considerations.
Ethical challenges arise when opinions on what is considered right and wrong diverge.
The Mission of the DataEthics4All Initiative is to raise awareness on the Ethical use of Data, build the next generation of Inclusive AI and be a Catalyst in building a more Equitable, Inclusive, Culture Rich, Just and Ethical Society.
The Framework: 12 Ethical Pillars.
Presenting The 12 Ethical Pillars Of The DataEthics4All Framework
1. Preserving Data Privacy
Preventing the Violation of Data Privacy and data mishandling by selling to third parties without consent.
2. Fair Data Processing
Making sure that the Data gathering and processing from multiple sources with the intention of combining these into a single data set for insights, trends and analysis is done fairly and without manipulation through aggregation.
That the Data Processing Power is applied with a clear understanding of the influence on the result and consequence on the data subject making sure that the Data is not drilled down to a point where the data subject can be easily identified.
3. Preventing Data Misuse
Making sure that there is no inappropriate use of data as perceived by the data subject as defined when the data was initially collected.
4. Clear Data Ownership
Clear understanding of ownership and accountability of personal identifiable information. (PII)
5. Explicit Data Consent
Making sure that there is an explicit data consent for the personal information obtained directly from a customer and both parties are fully aware of the use and benefits of that data.
6. Data Power and Control
Stopping Companies from unlimited control over personal data and on the non-transparent and uncontrolled proliferation of data transactions.
7. Data Transparency and Trust
Giving Customers control of their personal data and earning their Trust and Goodwill.
8. Data Quality Auditing
Preventing Unfair Discrimination by auditing the quality of data and making sure that the available data is representative of the whole population or phenomenon of study.
9. Interdisciplinary Algorithmic Auditing
Through Interdisciplinary Algorithmic Auditing we can detect Confirmation and other Biases in Artificial Intelligence Models and make sure there are no unwanted consequences.
10. Combating Deliberate Disinformation
Combating deliberate disinformation such as Fake News and Misleading Memes by making sure our Technology verifies the Sources of News before amplifying it.
11. Preventing Ad Technology Weaponization
Making sure that our Social Media Platforms and Ad Technologies are not being used for Political and Personal Gains.
12. Evolution of Data Ethics in Times of Crisis
Understanding the Pros and Cons of the Evolution of Data Ethics: Privacy, Consent, Transparency, etc in times of crisis.
Help detect crisis driven scams and other attacks on data based processes and prevent introduction of nefarious malware that could impact the outcome of data analysis.
How We Are Doing It?
1. Data Ethics Advisory Council
The Data Ethics Advisory Council consists of multidisciplinary experts that provide guidance on the Responsible Development of Data Science Ethics and help in setting a strategic roadmap for DataEthics4All.
The Data Ethics Advisory Council has also come up with The DataEthics4All Framework that can be used as Guiding Principles for any Organization to identify the gaps and mitigate the risks of using data science unethically within the Company enabling them to bring a Data Ethics First Culture to their Organization’s DNA.
2. Engaging Industry Experts
We engage credible Industry Experts from various fields to understand and keep up with Data Science Ethics: Best Practices and Trends.
3. Providing Thought Leadership
We bring Informed Opinion Leaders and the Go-to People in their field of expertise. We bring trusted sources that have the ability to move, inspire and influence people based on their passion for work and domain level expertise.
4. Fostering Community Discussions
The DataEthics4All Community fosters a common platform and levels the playing field for people to come together and openly share their views and concerns on the subject of Data Science Ethics as well as the Challenges of Data Compliance, Governance and Data Privacy for Businesses and Consumers.
5. Conducting Market Research
We use scientifically led studies to collect necessary market information from Business and Data Science Leaders enabling the stakeholders to be aware of the Best Practices, Trends and Challenges in Data Ethics.
6. Enabling Learning Opportunities
DataEthics4All enables blended and connected learning that combines personal interests, supportive relationships and opportunities.
Responsible development of Ethics in Data Science.