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
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- 06 Nov 2021
TL;DR: In this article, three lenses to explore the changing identity of the teacher are presented via a theoretical discussion paper: a Learning Analytics lens, a Media and Communications lens, and an Educational lens.
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•Posted Content
Artificial Tikkun Olam: AI Can Be Our Best Friend in Building an Open Human-Computer Society.
TL;DR: It is proposed that universities may consider forming interdisciplinary Study of Future Technology Centers to investigate and predict the fuller range of risks posed by technology and seek both common and AI specific solutions using computational, technical, conceptual and ethical analysis.
FAIR-FATE: Fair Federated Learning with Momentum
TL;DR: FAIR-FATE as mentioned in this paper is a fair federated learning algorithm that aims to achieve group fairness while maintaining high utility via a fairness-aware aggregation method that computes the global model by taking into account the fairness of the clients.
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A General Framework for Fair Regression.
TL;DR: In this paper, the authors consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision tree regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques.
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Artificial Intelligence ante portas: Reactions of Law
Rolf H. Weber
- 06 Sep 2021
TL;DR: In this paper, the authors argue for a combination of regulatory models (hard law and soft law), based on this assessment, the recent European legislative initiatives are analyzed, and they argue that fundamental legal principles (such as non-discrimination, human rights, transparency) need to be strengthened by regulatory interventions.
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