DTS 3182 units300 LevelFirst Semester

Ethics and Legal Issues in Data Science

B.Sc. Data Science, University of Uyo

The obligations that come with holding and modelling data about people: privacy law, consent, algorithmic fairness, transparency and accountability. The course that determines whether your technical skill does good or harm.

On Areté, DTS 318 comes with

  • 3 recommended textbooks
  • an AI tutor that has read this course’s outline and notes
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Course outline

  1. 01Ethical frameworks applied to data: consequentialist, deontological and virtue perspectives
  2. 02Privacy: informational self-determination, consent, purpose limitation and data minimisation
  3. 03Anonymisation, pseudonymisation and re-identification risk; k-anonymity and differential privacy in outline
  4. 04Data protection law: the Nigeria Data Protection Act, the NDPR, and the GDPR as a comparator
  5. 05Rights of data subjects: access, rectification, erasure, portability and objection
  6. 06Algorithmic bias and fairness: sources of bias, fairness metrics, and their trade-offs
  7. 07Transparency, explainability and the right to an explanation; black-box models in high-stakes decisions
  8. 08Accountability, auditing, and the governance of automated decision-making
  9. 09Professional codes of conduct and case studies of data science failures

Recommended textbooks

  • Weapons of Math Destruction — Cathy O'Neil

    The essential account of how models cause real-world harm

  • Ethics and Data Science — Loukides, Mason & Patil

    Short, free, and practical on day-to-day decisions

  • Fairness and Machine Learning — Barocas, Hardt & Narayanan

    Free at fairmlbook.org; the rigorous treatment of fairness metrics

How to pass DTS 318

  • Know the Nigeria Data Protection Act and the NDPR specifically — Nigerian law is what you are examined on and what will actually govern your work
  • Learn why the different fairness metrics are mathematically incompatible; "just make it fair" is not an available option and examiners want you to know that
  • Prepare three real case studies (e.g. COMPAS, a credit-scoring case, a facial-recognition case) in enough detail to analyse rather than merely name
  • This is the course whose content you will most often actually need after graduation — treat it as professional training, not a compliance box

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