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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
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TL;DR: A Fairness-Aware Re-ranking algorithm (FAR) is proposed to balance the ranking quality and provider-side fairness and can significantly promote fairness with a slight sacrifice in accuracy and can do so while being attentive to individual user differences.
A data-driven software tool for enabling cooperative information sharing among police departments
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TL;DR: An evaluation using human subjects showed that the CSS software provided significantly better support than a conventional database, and the modeling framework developed in this work is versatile, potentially useful for applications beyond law enforcement.
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On the (im)possibility of fairness
TL;DR: In this article, the authors show that in order to prove desirable properties of the entire decision-making process, different mechanisms for fairness require different assumptions about the nature of the mapping from construct space to decision space.
•Proceedings Article
On Fairness and Calibration
Geoff Pleiss,Manish Raghavan,Felix Wu,Jon Kleinberg,Kilian Q. Weinberger +4 more
- 06 Sep 2017
TL;DR: It is shown that calibration is compatible only with a single error constraint, and that any algorithm that satisfies this relaxation is no better than randomizing a percentage of predictions for an existing classifier.
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them
Hila Gonen,Yoav Goldberg +1 more
- 01 Jun 2019
TL;DR: This article showed that the gender bias information is still reflected in the distances between gender-neutralized words in the debiased embeddings, and can be recovered from them, and concluded that existing bias removal techniques are insufficient, and should not be trusted for providing gender neutral modeling.