Title: Overview
Course: Advanced Data Science: A Graduate Course > Section: Module 7: Data and AI ethics and governance

Module 7: Data and AI ethics and governance

Overview

In this module you assess bias, fairness, privacy, and governance using a recognized framework, and you apply it not to a toy example but to a realistic, self-contained case: a classifier used to make decisions about people, built on administrative records repurposed for this use without the affected individuals’ consent. You will quantify who the model helps and who it harms, learn why no model can satisfy every reasonable definition of fairness at once, and place your findings inside the NIST AI Risk Management Framework so that an ethical concern becomes a documented, auditable engineering artifact rather than a private worry. The work culminates in a role-played governance review board where you defend a live approve, reject, or condition decision about the model under review. This module serves outcome O6 (assess the ethical dimensions of a data problem using a recognized governance framework) and O5 (communicate model behavior and limitations to technical and non-technical stakeholders).

By the end of this module

- Audit a trained classifier for disparate impact by computing subgroup performance and a formal disparate-impact ratio.
- Explain, with reference to a specific deployment, why common fairness metrics mathematically conflict and cannot be jointly satisfied except in degenerate cases.
- Apply the four functions of the NIST AI Risk Management Framework (Govern, Map, Measure, Manage) to map identified harms to concrete controls.
- Complete a Model Card and a Datasheet for the Dataset that document a model’s behavior, limitations, and data provenance and consent.
- Recommend and defend a proportionate approve, reject, or condition governance decision, naming the tradeoffs each mitigation imposes.

Readings

- Fairness and Machine Learning, Barocas, Hardt, and Narayanan (link). Read the Introduction and the Classification chapter. Focus on the formal definitions of group fairness criteria and the demonstration that independence, separation, and sufficiency cannot generally coexist; this is the mathematical backbone of the whole module.
- Data Feminism, D’Ignazio and Klein (open access). Read “Examine Power” and “Consider Context.” Focus on how the choice of what to count, who is classified, and whose experience is treated as default embeds power relations before any model is trained.
- NIST AI Risk Management Framework (public domain). Focus on the four core functions and their categories; you will map the case model’s harms onto them in the assessment, so read for the structure and vocabulary, not for memorization.
- Data Science Code of Professional Conduct (link). Focus on the sections on competence, confidentiality, and the duty to disclose limitations; these give you professional language for the obligations the technical work implies.

“Advanced Data Science by Michelle Blomberg is licensed under CC BY-NC-SA 4.0.”