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
Towards Equalised Odds as Fairness Metric in Academic Performance Prediction
Jannik Dunkelau,Manh Khoi Duong +1 more
TL;DR: This paper takes a closer look at academic performance prediction (APP) systems and suggests equalised odds as most suitable notion for APP, based on APP’s WYSIWYG worldview as well as potential long-term improvements for the population.
A relationship and not a thing: A relational approach to algorithmic accountability and assessment documentation
TL;DR: In this article , the authors argue that robust accountability regimes must establish opportunities for publics to cohere around shared experiences and interests, and to contest the outcomes of algorithmic systems that affect their lives.
•Proceedings Article
Fairness without Harm: Decoupled Classifiers with Preference Guarantees
Berk Ustun,Yang Liu,David C. Parkes +2 more
- 24 May 2019
TL;DR: It is argued that when there is this kind of treatment disparity then it should be in the best interest of each group, and a recursive procedure is introduced that adaptively selects group attributes for decoupling to ensure preference guarantees in terms of generalization error.
•Posted Content
A Distributionally Robust Approach to Fair Classification.
TL;DR: A distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity is proposed and it is demonstrated that the resulting classifier improves fairness at a marginal loss of predictive accuracy on both synthetic and real datasets.
A Critical Survey on Fairness Benefits of Explainable AI
Luca Deck,Jakob Schoeffer,Maria De-Arteaga,Niklas Kuhl +3 more
- 15 Oct 2023
TL;DR: A critical survey on fairness benefits of explainable AI finds that claims about its fairness benefits are often vague, lacking normative grounding, or poorly aligned with actual capabilities.
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