Knowledge base

Our knowledge base is a collection of readworthy articles, books and other publications from various disciplines about algorithmic fairness. Each piece of writing has been summarized and/or reviewed.

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White paper – Feedback on DSA Delegated Regulation (conducting independent audits)

Author(s): Algorithm Audit

Plea to include the normative dimension of AI auditing in delegated regulation of the Digital Services Act (DSA). Current limitations are illustrated by focussing on a recommender systems example

Discrimination through Risk Profiling

Author(s): Dutch College for Human Rights

Framework for discrimination through risk-profiling

Practical Fairness

Author(s): Aileen Nielsen

A wide-ranging book that informs data and AI ethics practitioners about recent advances in open-source toolkits and legal frameworks.

Protected grounds and the system of non-discrimination law in the context of algorithmic decision-making and AI

Author(s): Janneke Gerards and Frederik Zudierveen Borgesius

Plea that a hybrid system of non-discrimination law, with a semi-closed list of grounds and an open possibility for exemptions and justification, is best-suited to deal with the particularities of AI-driven discrimination.

The Ethical Algorithm

Author(s): Michael Kearns, Aaron Roth

Technically accesible and compelling book. Filled with fresh thoughts from a frontier in the fair ML community. Written by two top-tier computer scientists.

The Measure and Mismeasure of Fairness

Author(s): Sam Corbett-Davies, Sharad Goel

Why are formulas for fairness troublesome? Why prevailing definitions of fairness typically do not map on to traditional social, economic or legal understandings of the concept? This paper provides answers.

Why Fairness cannot be Automated

Author(s): Sandra Wachter, Brent Mittelstadt, Chris Russell

Must-read scientific paper proposing ideas to bridge the qualitative (legal) and quantitative (statistical) doctrine