# Algorithmic Fairness

@article{Pessach2020AlgorithmicF, title={Algorithmic Fairness}, author={Dana Pessach and Erez Shmueli}, journal={ArXiv}, year={2020}, volume={abs/2001.09784} }

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be…

## 228 Citations

### CONFAIR: Configurable and Interpretable Algorithmic Fairness

- Computer Science
- 2021

The CONFAIR procedure is proposed that produces a fair algorithm by incorporating user constraints into the optimization procedure and is demonstrated the efficacy of the approach on several real world datasets using different fairness criteria.

### FAIRLEARN: Configurable and Interpretable Algorithmic Fairness

- Computer ScienceArXiv
- 2021

The FAIRLEARN procedure is proposed that produces a fair algorithm by incorporating user constraints into the optimization procedure and is demonstrated the efficacy of the approach on several real world datasets using different fairness criteria.

### Coping with Mistreatment in Fair Algorithms

- Computer ScienceArXiv
- 2021

This paper studies the algorithmic fairness in a supervised learning setting and examines the effect of optimizing a classifier for the Equal Opportunity metric and proposes a conceptually simple method to mitigate this bias.

### A Statistical Test for Probabilistic Fairness

- Computer ScienceFAccT
- 2021

This paper develops a rigorous hypothesis testing mechanism for assessing the probabilistic fairness of any pre-trained logistic classifier, and shows both theoretically as well as empirically that the proposed test is asymptotically correct.

### Algorithmic Fairness in a Technology-Based World

- Computer Science
- 2021

This literature review seeks to break the field into its sub-components and delve into the research being done in each and convey the real-world impacts decision-making algorithms are increasingly involved in across a range of domains.

### On the Advantages of Distinguishing Between Predictive and Allocative Fairness in Algorithmic Decision-Making

- Computer ScienceMinds and Machines
- 2022

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 morally permissible, but it is argued that this is conceptually misguided and that the problem should be replaced with two sub-problems.

### Algorithm Design: Fairness and Accuracy

- Computer Science, Economics
- 2021

This work defines and characterize a fairness-accuracy frontier, consisting of the optimal points across a broad range of criteria for trading off fairness and accuracy, and identifies how the algorithm’s inputs govern the shape of this frontier.

### Algorithmic Design: Fairness Versus Accuracy

- Computer ScienceEC
- 2022

A model in which a designer chooses an algorithm that maps observed inputs into decisions is proposed, and a fairness-accuracy Pareto frontier is introduced, showing (for example) that access to group identity reduces the error for the worse-off group everywhere along the frontier.

### An Exploratory Study on Fairness-Aware Design Decision-Making

- Computer ScienceHICSS
- 2022

This paper quantifies unfairness and analyzes its impact in the context of data-driven engineering design using the Adult Income dataset, and introduces a fairness-aware design concept and standard definitions and statistical measures of fairness to the engineering design research.

### Matching code and law: achieving algorithmic fairness with optimal transport

- Computer ScienceData Mining and Knowledge Discovery
- 2019

The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence.

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