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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.
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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
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
Alex Beutel,Jilin Chen,Tulsee Doshi,Hai Qian,Allison Woodruff,Christine Luu,Pierre Kreitmann,Jonathan Bischof,Ed H. Chi +8 more
- 27 Jan 2019
TL;DR: In this paper, the authors provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues.
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Censoring Representations with an Adversary
Harrison Edwards,Amos Storkey +1 more
TL;DR: In this paper, the adversarial model is formulated as a minimax problem, and the objective is to minimize the performance of the adversary while ensuring that there is little or no information in the representation about the sensitive variable.
106
A Short-term Intervention for Long-term Fairness in the Labor Market
Lily Hu,Yiling Chen +1 more
- 10 Apr 2018
TL;DR: A dynamic reputational model of the labor market is constructed that illustrates the reinforcing nature of asymmetric outcomes resulting from groups» divergent accesses to resources and as a result, investment choices and Pareto-dominates those arising from strategies that statistically discriminate or employ a "group-blind" criterion.
Runaway Feedback Loops in Predictive Policing
Danielle Ensign,Sorelle A. Friedler,Scott Neville,Carlos Scheidegger,Suresh Venkatasubramanian +4 more
- 21 Jan 2018
TL;DR: In this paper, the authors developed a mathematical model of predictive policing that proves why this feedback loop occurs, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned.
Active Fairness in Algorithmic Decision Making
Alejandro Noriega-Campero,Michiel A. Bakker,Bernardo Garcia-Bulle,Alex Pentland +3 more
- 27 Jan 2019
TL;DR: This paper proposed an alternative active framework for fair classification, where, in deployment, a decision-maker adaptively acquires information according to the needs of different groups or individuals, towards balancing disparities in classification performance.
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