There are many dangers to society if data science is used without ethical considerations.
DataEthics4Allᵀᴹ is on a Mission to raise awareness about the ethical use of data and AI; and tackle inclusion, equity and diversity in tech through a grassroots approach. We’re celebrating Ethics 1stᵀᴹ Leaders of today and creating Ethics 1stᵀᴹ Leaders of tomorrow.
We promote a wide range of Programs, and foster a Community of 1000+ Global Leaders in technology, data, ethics, and policy.
Take a look at our framework and the 12 ethical pillars of the DataEthics4Allᵀᴹ Framework 2.0, and meet some of our Academic and Institutional Partners.
Join us in building a better AI DIETᵀᴹ World, a World where there’s privacy and trust across the entire data pipeline, diversity and inclusion in teams, ethics in company cultures and practices, transparency and explainability in technology!
Grade 6-12 Student
Schools of our Tutors and Students
Schools of our Tutors and Students
1. Building responsible tech with ethics woven into the fabric by design.
2. Promoting end-to-end data governance across organizations to guarantee ethical data use.
3. Ensuring cognitive diversity (demographic and functional diversity) and inclusion in human oversight for building and auditing the next generation of AI models and systems.
The 12 Ethical Pillars.
Presenting The 12 Ethical Pillars Of The DataEthics4All Framework 2.0
1. Preserving Data Privacy
Preventing the violation of data privacy and data mishandling through non-consensual sale to third parties.
2. Fair Data Processing
Ensuring that data gathering and processing from multiple sources is done fairly and without manipulation through aggregation.
Ensuring that power in data processing is applied with a clear understanding of the influence on the results and consequences on the data subjects.
Ensuring that individual data subjects are able to retain their anonymity.
3. Preventing Data Misuse
Ensuring there is no inappropriate use of data as perceived by the data subject when data was initially collected.
4. Clear Data Ownership
Advocating for clear understanding of ownership and accountability of personally identifiable information. (PII)
5. Explicit Data Consent
Ensuring there is explicit data consent for the personal information obtained directly from a customer, and that all parties are fully aware of the use and benefits of that data.
6. Data Power and Control
Preventing companies from taking unlimited control over personal data.
Preventing 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 ensuring that the available data is representative of the whole population or phenomenon of study.
9. Interdisciplinary Algorithmic Auditing
Detecting confirmation biases and other biases in Artificial Intelligence Models through Interdisciplinary Algorithmic Auditing.
10. Combating Deliberate Disinformation
Combating deliberate disinformation via fake news and misleading memes by making sure our technology verifies news sources before amplifying them.
11. Preventing Ad Technology Weaponization
Ensuring that our social media platforms and ad technologies are not being used for political and personal gain.
12. Evolution of Data Ethics in Times of Crisis
Understanding the evolution of data ethics, privacy, consent, transparency, etc., in times of crisis.
Detecting crisis-driven scams and other attacks on data-based processes.
Preventing the introduction of nefarious malware which could impact the outcome of data analysis.
What are our methods to achieve this?
1. Data Ethics Advisory Council
The Data Ethics Advisory Council consists of multidisciplinary experts who provide guidance on the responsible development of data science ethics and set a strategic roadmap for DataEthics4All.
The Data Ethics Advisory Council has also authored the DataEthics4All Framework, which can be used by any organization as guiding principles to mitigate the risks of using data science unethically.
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 work with informed opinion leaders and trusted sources who have the ability to move, inspire and influence.
4. Fostering Community Discussions
The DataEthics4All Community platform levels the playing field for individuals to come together and openly share their views and concerns on the subject of data science ethics and the many challenges of data compliance, governance and data privacy for businesses and consumers.
5. Conducting Market Research
We employ scientifically-led studies to enable stakeholders to be aware of trends and challenges in data ethics, and to generate solutions.
6. Enabling Learning Opportunities
DataEthics4All enables blended and connected learning which combines personal interests, supportive relationships, and opportunities.
Responsible development of Ethics in Data Science.