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
The Role of Human Underwriting in the Big Data Era
Janet Gao,Hanyi Yi,David Hao Zhang +2 more
TL;DR: This study examines the role of human underwriting in the big data era, highlighting the limitations of automated underwriting systems and the importance of human judgment in complex risk assessment, particularly in high-stakes insurance decisions.
Digital gold? Pricing, inequality and participation in data markets*
TL;DR: Goyal et al. as discussed by the authors examined inequalities arising from biases brought by the incentives and externalities present in data markets, where a data collector ultimately provides an end-service which is bene cial.
Lq regularization for fair artificial intelligence robust to covariate shift
TL;DR: In this article , a robust fairness constraint based on the Lq norm is proposed to ensure fairness on test data, which is a generic algorithm to be applied to various fairness AI problems without much hampering.
•Posted Content
Towards a Flexible Framework for Algorithmic Fairness
TL;DR: An algorithm is presented that harnesses optimal transport to provide a flexible framework to interpolate between different fairness definitions and shows that important normative and legal challenges remain for the implementation of algorithmic fairness interventions in real-world scenarios.
Harm Ratio: A Novel and Versatile Fairness Criterion
Soroush Ebadian,Rupert Freeman,Nisarg Shah +2 more
- 23 Oct 2024
TL;DR: This paper introduces the harm ratio, a novel fairness criterion inspired by envy-freeness, applicable to various collective decision-making settings, and theoretically and empirically evaluates its properties and computational complexity.
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