The following is a starter list of resources about ethics in data science - including data aqcuisition, security, privacy, fairness and accountability of algorithms, moral issues, legal and regulatory considerations, and more. You are expected to have a section about ethics in your project presentation.
- MIT's Tech Review: Biased Algorithms Are Everywhere, and No One Seems to Care link
- Data Science Ethics: edX MOOC from the University of Michigan
- Ted Talk by author of the book: Weapons of Math Destruction The era of blind faith in big data must end
- Machine intelligence makes human morals more important: excellent TED talk by Zeynep Tufekci
- Fighting bias in algorithms: another great TED talk (around 9 minutes). Speaker runs AJL.
- Kate Crawford:The Trouble with Bias - keynote, from NIPS 2017
- Keynote of Ethical Machine Learning: Jupyter notebook and list of related talks, resources and articles
- AI and Ethics: Overcoming the Risks
- What are the ethical considerations of machines learning?: Question on Quora with different answers
- What is the ethical responsibility of data scientists?
- Ethical Considerations in Artificial Intelligence Courses: excellent paper and interesting questions
- Making Hard Choices: The Quest for Ethics in Machine Learning: LinkedIn blog
- Ethical and Privacy Issues in Data Science: youtube video on cookies, behavioral advertising and privacy
- Why Are There Still So Many Jobs?: from TEDx Cambridge. See also AI & The Future of Work: focus on emotion, empathy, creativity and critical thought and Artificial Intelligence: it will kill us.
- Ethical Issues in Data Science: talk focusing on biomedical data
- Ethics & Data Science: keynote at DataEDGE 2014
- AI Ethics Resources by Rachel Thomas of Fast.Ai
- You can also watch this video for insights and fun
Last Updated: Sep 25, 2018