Academic and public debates are increasingly concerned with the question whether and
how algorithmic decision-making (ADM) may reinforce social inequality. Most previous …

Data practices shape research and practice on fairness in machine learning (fair ML).
Critical data studies offer important reflections and critiques for the responsible …

Prediction algorithms are regularly used to support and automate high-stakes policy
decisions about the allocation of scarce public resources. However, data-driven decision …

" The Handbook of Computational Social Science is a comprehensive reference source for
scholars across multiple disciplines. It outlines key debates in the field, showcasing novel …

A vast number of systems across the world use algorithmic decision making (ADM) to
(partially) automate decisions that have previously been made by humans. The downstream …

Considerable research effort has been guided towards algorithmic fairness but real-world
adoption of bias reduction techniques is still scarce. Existing methods are either metric-or …

Applications of machine learning (ML) to high-stakes policy settings-such as education,
criminal justice, healthcare, and social service delivery-have grown rapidly in recent years …

The increasing use of algorithms in allocating resources and services in both private
industry and public administration has sparked discussions about their consequences for …

Abstract National Statistical Organizations (NSOs) increasingly draw on Machine Learning
(ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML …

Data valuation is a ML field that studies the value of training instances towards a given
predictive task. Although data bias is one of the main sources of downstream model …