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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How 'algoprudence' can contribute to responsible use of ML-algorithms
Author(s): Anne Meuwese, Jurriaan Parie & Ariën Voogt
By means of two case positions regarding the use of machine learning-driven risk profiling by the municipalities of Rotterdam and Amsterdam, the concept of 'algoprudence' is introduced and explained.
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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.
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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.
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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.
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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
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