12 Ethics and Privacy
In this block we cover:
- What laws govern data science?
- What is Ethical data science?
- What is privacy?
- How can we design data science tools to protect it?
- What is Statistical discosure?
- An introduction to differential privacy…
- What is interpretability in terms of machine learning algorithms?
- What is algorithmic bias? How can we mitigate it?
- Ethics in Data Science
- Laws governing data science:
- The Algorithmic Foundations of Differential Privacy by Dwork and Roth (2014).
- ONS policy on disclosure control.
- Sweeney 1997. Weaving technology and policy together to maintain confi-dentiality. Journal of Law, Medicines Ethics, 25:98–110.
- Narayanan and Shmatikov 2008. Robust de-anonymization of largesparse datasets (how to break anonymity of the netflix prize dataset). IEEE Sec. and Priv.
- Statistical Disclosure Attacks by George Danezis.
- Interpretability and fairness:
- Book: “Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.” Christoph Molnar 2019.
- Algorithmic Bias Tutorial by Francesco Bonchi with Slides from KDD 2016
- Hardt, Price and Srebo Equality of Opportunity in Supervised Learning 2016 explored in https://blog.acolyer.org/2018/05/07/equality-of-opportunity-in-supervised-learning/.
Previous: Block 11.