Reading Group Summary: How the machine ‘thinks’: Understanding opacity in machine learning algorithms

How does the problem of opacity in machine learning algorithms affect the mechanisms of ranking and classification? For example, determining whether an email is spam, identifying possible credit card fraud, news trends, credit scoring, etc. These mechanisms of classification and ranking derive from machine learning algorithms. Burrell examines 3 kinds of opacity: Intentional opacity for […]

Reading Group Summary: Fairness and Abstraction in Sociotechnical Systems

The paper “Fairness and Abstraction in Sociotechnical Systems” by Selbst et al., 2019, focuses on the idea of ‘fairness’ in Machine Learning (ML) – where an algorithm is making decisions about a social situation (e.g., a judge evaluating a parolee’s flight risk).  It describes different problems that users of ML systems confront that can affect […]