11 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 disclosure?
  • An introduction to differential privacy…
• What is interpretability in terms of machine learning algorithms?
• What is algorithmic bias? How can we mitigate it?

Lectures

• Data Science in the law
• Statistical Disclosure
• Fairness

  Worksheets:

• Worksheet 11.1 Ethics and Privacy in Data Science
• Portfolio 11

  References:

• Laws governing data science:
  • Human Rights Act (HRA 1998)
  • EU General Data Protection Regulation (GDPR 2018)
  • Data Protection Act 2018 (DPA 2018)
• Privacy:
  • 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/.

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