As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

The widespread usage of machine learning systems and econometric methods in the credit
domain has transformed the decision-making process for evaluating loan applications …

The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …

Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …

Recent scholarship has brought attention to the fact that there often exist multiple models for
a given prediction task with equal accuracy that differ in their individual-level predictions or …

Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …

Following precedent in employment discrimination law, two notions of disparity are widely-
discussed in papers on fairness and ML. Algorithms exhibit treatment disparity if they …

Data-driven decision making related to individuals has become increasingly pervasive, but
the issue concerning the potential discrimination has been raised by recent studies. In …

When consequential decisions are informed by algorithmic input, individuals may feel
compelled to alter their behavior in order to gain a system's approval. Models of agent …

We propose a formal framework for interpretable machine learning. Combining elements
from statistical learning, causal interventionism, and decision theory, we design an idealised …