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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
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward:
TL;DR: There is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management, and four challenges in using data science techniques for HR management are identified.
848
•Posted Content
Fairness in Machine Learning: A Survey.
Simon Caton,Christian Haas +1 more
TL;DR: An overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature is provided, organises approaches into the widely accepted framework of pre-processing, in- processing, and post-processing methods, subcategorizing into a further 11 method areas.
Algorithmic Fairness: Choices, Assumptions, and Definitions
TL;DR: It is shown how choices and assumptions made—often implicitly—to justify the use of prediction-based decision-making can raise fairness concerns and a notationally consistent catalog of fairness definitions from the literature is presented.
491
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Harini Suresh,John V. Guttag +1 more
- 05 Oct 2021
TL;DR: In this paper, the authors identify seven potential sources of downstream harm in machine learning, spanning data collection, development, and deployment, and propose a framework to facilitate more productive and precise communication around these issues.
417
References
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The Frontiers of Fairness in Machine Learning
TL;DR: This report summarizes the findings of a group of experts convened as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward.
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A Reductions Approach to Fair Classification
Alekh Agarwal,Alina Beygelzimer,Miroslav Dudík,John Langford,Hanna Wallach +4 more
- 03 Jul 2018
TL;DR: In this paper, the authors present a systematic approach for achieving fairness in a binary classification setting, which reduces fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints.
The cost of fairness in binary classification
Aditya Krishna Menon,Robert C. Williamson +1 more
- 21 Jan 2018
TL;DR: This work relates two existing fairness measures to cost-sensitive risks, and shows that for such costsensitive fairness measures, the optimal classifier is an instance-dependent thresholding of the class-probability function.
•Proceedings Article
Learning Adversarially Fair and Transferable Representations
David Madras,Elliot Creager,Toniann Pitassi,Richard S. Zemel +3 more
- 03 Jul 2018
TL;DR: In this article, adversarial representation learning is used to ensure that the learned representations admit fair predictions on new tasks while maintaining utility, which is an essential goal of fair representation learning.
•Proceedings Article
Fairness in learning: classic and contextual bandits
Matthew Joseph,Michael Kearns,Jamie Morgenstern,Aaron Roth +3 more
- 05 Dec 2016
TL;DR: In this article, the authors introduce the notion of fairness in multi-armed bandit problems, and prove a worst-case exponential gap in regret between fair and non-fair learning algorithms.