Prediction-based decision-making systems are becoming increasingly prevalent in various
domains. Previous studies have demonstrated that such systems are vulnerable to runaway …

Predictions in the social world generally influence the target of prediction, a phenomenon
known as performativity. Self-fulfilling and self-negating predictions are examples of …

Organisations generate vast amounts of information, which has resulted in a long-term
research effort into knowledge access systems for enterprise settings. Recent developments …

We argue that insurance can act as an analogon for the social situatedness of machine
learning systems, hence allowing machine learning scholars to take insights from the rich …

Recent work on the limitations of using reinforcement learning from human feedback (RLHF)
to incorporate human preferences into model behavior often raises social choice theory as a …

Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) follow a familiar
structure: A firm releases a large, pretrained model. It is designed to be adapted and …

Machine learning models are often used to decide who will receive a loan, a job interview,
or a public benefit. Standard techniques to build these models use features about people but …

The power of digital platforms is at the center of major ongoing policy and regulatory efforts.
To advance existing debates, we designed and executed an experiment to measure the …

Collective action in Machine Learning is the study of the control that a coordinated group
can have over machine learning algorithms. While previous research has concentrated on …

Abstract Machine learning ethics research is demonstrably skewed. Work that defines
fairness as a matter of distribution or allocation and that proposes computationally tractable …