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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
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition
Tomáš Sixta,Julio C. S. Jacques Junior,Pau Buch-Cardona,Eduard Vazquez,Sergio Escalera +4 more
- 23 Aug 2020
TL;DR: The 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge as mentioned in this paper evaluated accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes.
Bias In, Bias Out? Evaluating the Folk Wisdom
Ashesh Rambachan,Jonathan Roth +1 more
TL;DR: Whether a prediction algorithm reverses or inherits bias depends critically on how the decision-maker affects the training data as well as the label used in training.
Fair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling
TL;DR: The results reveal that mode choice models are indeed affected by algorithmic bias, and it is proven that the implementation of off-the-shelf mitigation techniques allows us to achieve fairer classification models.
Peer Review
Are Algorithms Biased in Education? Exploring Racial Bias in Predicting Community College Student Success
TL;DR: In this article , the authors provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models -one predicting course completion, the second predicting degree completion -and show that algorithmic biases in both models could result in at-risk Black students receiving fewer success resources than white students at comparatively lower risk of failure.
The Impact of Technology Change in Work, Employment and HRM
TL;DR: Technological change has significantly impacted work, employment, and HRM, leading to automation, remote working, and algorithmic decision-making. However, it also creates challenges such as inequality and ethical concerns. To address these challenges, HR departments need to implement practical interventions.
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