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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
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
Zhihong Deng,Jing Jiang,G Long,Chengqi Zhang +3 more
TL;DR: The paper explores fairness in reinforcement learning by investigating the sources of inequality and introducing a novel notion called dynamics fairness. It analyzes causal relationships and decomposes the effect of sensitive attributes on long-term well-being into distinct components. The paper also introduces identification formulas for quantifying dynamics fairness and demonstrates its effectiveness in explaining, detecting, and reducing inequality.
Algorithmic Design: Fairness Versus Accuracy
Annie Liang,Jay Lu,Xiaosheng Mu +2 more
- 12 Jul 2022
TL;DR: A model in which a designer chooses an algorithm that maps observed inputs into decisions is proposed, and a fairness-accuracy Pareto frontier is introduced, showing (for example) that access to group identity reduces the error for the worse-off group everywhere along the frontier.
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias
TL;DR: This work studies fairness considerations of active data collection strategies in the presence of label bias and empirically shows that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem.
Bias and Non-Diversity of Big Data in Artificial Intelligence: Focus on Retinal Diseases
Cris Martin P. Jacoba,Leo Anthony Celi,Alice C. Lorch,Ward Fickweiler,Lucia Sobrin,Judy Wawira Gichoya,Lloyd Paul Aiello,Paolo S. Silva +7 more
TL;DR: In this article , a lack of diversity in the available ophthalmic datasets, with 45% of the global population having no readily accessible representative images, leading to potential misrepresentations of their unique anatomic features and ocular pathology.
Identifying Spurious Correlations for Robust Text Classification
Zhao Wang,Aron Culotta +1 more
- 01 Nov 2020
TL;DR: This paper treats this as a supervised classification problem, using features derived from treatment effect estimators to distinguish spurious correlations from “genuine” ones, and finds that the approach works well even with limited training examples, and that it is possible to transport the word classifier to new domains.
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