Algorithmic Fairness

By Dana Pessach, Erez Shmueli. 2020

448 citations recorded by the source corpus.

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.

Subfields: AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

1. Berk Ustun
2. Shira Mitchell
3. Eric Potash
4. Yang Liu
5. D. Parkes
6. Solon Barocas
7. Adrian Weller
8. Cynthia Rudin

Citations in the corpus

• Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “In making decisions about college admissions, it may be tempting to narrow the larger goals of higher education to simply the future GPAs of admitted students [73].”
• Human-Centered AI (2021, background): “At present, researchers are exploring the work of oversight and related issues, including regulatory policy and data ownership, through subfields such as responsible AI [20], [21] , algorithmic fairness [22] , ethical AI [23] , and explainable AI [24] .”
• Neurosymbolic AI: the 3rd wave (2020, unknown): “Program synthesis based more generally on probabilistic languages other than logic have also flourished as part of the research area known as neurosymbolic programming (Chaudhuri et al. 2021).”
• Fairness without Harm: Decoupled Classifiers with Preference Guarantees (2019, background): “In addition to Zafar et al. (2017b) and Dwork et al. (2018), several other works discuss treatment disparity to achieve parity-based notions of fairness (see e.g., Corbett-Davies & Goel, 2018; Kleinberg et al., 2018; Lipton et al., 2018; Wang et al., 2019).”