Open AccessPosted Content
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
Towards fair machine learning using combinatorial methods
TL;DR: A notion of fairness is developed that is based on the notion of discrepancy of set systems, a widely studied topic in the theory of computer science and combinatorics.
3
•Posted Content
Fair Clustering for Diverse and Experienced Groups.
TL;DR: A fair clustering variant where the input data is a hypergraph with multiple edge types, representing information about past experiences of groups of individuals, with a goal of forming groups that have both experience and diversity with respect to participation in edge types.
3
Towards Trustworthy and Understandable AI: Unraveling Explainability Strategies on Simplifying Algorithms, Appropriate Information Disclosure, and High-level Collaboration
Shuren Yu
- 03 Oct 2023
TL;DR: Unraveling explainability strategies to simplify algorithms, disclose appropriate information, and facilitate high-level collaboration for trustworthy and understandable AI systems.
2
Algorithmic Bias and Racial Inequality: A Critical Review
Maximilian Kasy
TL;DR: This critical review argues that algorithmic bias definitions prioritize profit over disadvantaged groups' interests, suggesting a shift to focus on welfare distribution and causal effects of automated decisions on racial minorities.
2
Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy
Limor Gultchin,Vincent Cohen-Addad,Sophie Giffard-Roisin,V.M. Kanade,Frederik Mallmann-Trenn +4 more
TL;DR: This paper proposes an objective that aims to balanceiency and separation measures, while maintaining similar accuracy levels, and shows the use of such an objective in two empirical case studies, one involving a multi-objective framework, and the other pre-tuning of a model pre-trained for accuracy.
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