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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
Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies
TL;DR: A comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies is provided in this article . But, the focus of this survey is on the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes.
110
Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs
TL;DR: This paper discusses the ethical foundations of each definition of fairness, and introduces a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes.
Algorithmic fairness datasets: the story so far
TL;DR: In this article , the authors focus on data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them.
Diagnosing Gender Bias in Image Recognition Systems
Carsten Schwemmer,Carly R. Knight,Emily Bello-Pardo,Stan Oklobdzija,Martijn Schoonvelde,Jeffrey W. Lockhart +5 more
- 11 Nov 2020
TL;DR: This article evaluates potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians to find that images of women received three times more annotations related to physical appearance.
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Evolution and impact of bias in human and machine learning algorithm interaction
TL;DR: It is argued that algorithmic bias interacts with humans in an iterative manner, which has a long-term effect on algorithms’ performance, and three different iterated bias modes, as well as initial training data class imbalance and human action, do affect the models learned by machine learning algorithms.
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