A clarification of the nuances in the fairness metrics landscape
In recent years, the problem of addressing fairness in machine learning (ML) and automatic
decision making has attracted a lot of attention in the scientific communities dealing with …
decision making has attracted a lot of attention in the scientific communities dealing with …
The statistical fairness field guide: perspectives from social and formal sciences
Over the past several years, a multitude of methods to measure the fairness of a machine
learning model have been proposed. However, despite the growing number of publications …
learning model have been proposed. However, despite the growing number of publications …
Fairsna: Algorithmic fairness in social network analysis
In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …
domains, including machine learning, natural language processing, and information …
Fragile algorithms and fallible decision-makers: lessons from the justice system
J Ludwig, S Mullainathan - Journal of Economic Perspectives, 2021 - aeaweb.org
Algorithms (in some form) are already widely used in the criminal justice system. We draw
lessons from this experience for what is to come for the rest of society as machine learning …
lessons from this experience for what is to come for the rest of society as machine learning …
Algorithmic unfairness through the lens of EU non-discrimination law: Or why the law is not a decision tree
Concerns regarding unfairness and discrimination in the context of artificial intelligence (AI)
systems have recently received increased attention from both legal and computer science …
systems have recently received increased attention from both legal and computer science …
A classification of feedback loops and their relation to biases in automated decision-making systems
Prediction-based decision-making systems are becoming increasingly prevalent in various
domains. Previous studies have demonstrated that such systems are vulnerable to runaway …
domains. Previous studies have demonstrated that such systems are vulnerable to runaway …
Bias on demand: a modelling framework that generates synthetic data with bias
Nowadays, Machine Learning (ML) systems are widely used in various businesses and are
increasingly being adopted to make decisions that can significantly impact people's lives …
increasingly being adopted to make decisions that can significantly impact people's lives …
On the advantages of distinguishing between predictive and allocative fairness in algorithmic decision-making
F Beigang - Minds and Machines, 2022 - Springer
The problem of algorithmic fairness is typically framed as the problem of finding a unique
formal criterion that guarantees that a given algorithmic decision-making procedure is …
formal criterion that guarantees that a given algorithmic decision-making procedure is …
From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making
Prediction algorithms are regularly used to support and automate high-stakes policy
decisions about the allocation of scarce public resources. However, data-driven decision …
decisions about the allocation of scarce public resources. However, data-driven decision …
The zoo of fairness metrics in machine learning
In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic
decision-making has attracted a lot of attention in the scientific communities dealing with …
decision-making has attracted a lot of attention in the scientific communities dealing with …