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Algorithmic Fairness
Dana Pessach,Erez Shmueli +1 more
TL;DR: An overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms is presented and the most commonly used fairness-related datasets in this field are described.
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Abstract: 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 inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.
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Figures
Table 1. Measures and Definitions for Algorithmic Fairness 
Table 4. Additional Measures and Definitions for Algorithmic Fairness 
Fig. 1. If the SAT scores were used for hiring, then unprivileged candidates with high potential would be excluded, whereas lower potential candidates from the privileged group would be hired instead 
Table 3. Common Benchmark Datasets for Algorithmic Fairness 
Table 2. Pre-Process, In-Process and Post-Process Mechanisms for Algorithmic Fairness
Citations
Refusal in Data Ethics: Re-Imagining the Code Beneath the Code of Computation in the Carceral State
TL;DR: In this article , the authors explore two complementary modalities of refusal in computation: "refusal as resistance" and "recentering the margins" and provide a vocabulary for identifying and rejecting the ways that sociotechnical systems reinforce dependency on oppressive structural conditions.
•Proceedings Article
(Individual) Fairness for k-Clustering
Sepideh Mahabadi,Ali Vakilian +1 more
- 12 Jul 2020
TL;DR: The $k-median ($k-means) cost of the solution is within a constant factor of the cost of an optimal fair $k$-clustering, and the solution approximately satisfies the fairness condition.
One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas
Vitaly Meursault,Daniel Moulton,Larry Santucci,Nathan Schor +3 more
- 01 Nov 2022
TL;DR: In this paper , the authors use an approach from the Fairness in Machine Learning literature, namely fairness constraints via group-specific prediction thresholds, and show that gaps in true positive rates (% of non-defaulters identified by the model as such) can be significantly reduced if separate thresholds can be chosen for non-LMI and LMI tracts.
A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes
Jiang Bian,Yu Huang,Jingchuan Guo,William T. Donahoo,Zhengkang Fan,Ying Lu,Wei-Han Chen,Huilin Tang,Lori Bilello,Aaron Saguil,Eric Rosenberg,Elizabeth Shenkman +11 more
TL;DR: A machine learning analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) and fairness optimization is developed.
Matching code and law: achieving algorithmic fairness with optimal transport.
TL;DR: 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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