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
•Proceedings Article
Finite Continuum-Armed Bandits
Solenne Gaucher
- 22 Oct 2020
TL;DR: Focusing on a nonparametric setting, where the mean reward is an unknown function of a one-dimensional covariate, this work proposes an optimal strategy for this problem and proves that the optimal regret scales as O(T^{1/3})$ up to poly-logarithmic factors when the budget $T$ is proportional to the number of actions.
The use of Maximum Completeness to Estimate Bias in AI-based Recommendation Systems
TL;DR: In this paper , the authors tried to identify how to measure the group fairness of a prediction of a classification algorithm, to identify the quality features of the dataset that influence the learning process, and finally, to evaluate the relationships between quality features and the fairness measures.
2
Discrimination for the Sake of Fairness: Fairness by Design and Its Legal Framework
TL;DR: A specific case is introduced and how EU law might apply when an algorithm accesses sensitive information to make fairer predictions is analyzed, finding that several legal claims could arise regarding the use of sensitive information.
2
Group decision making under uncertain preferences: powered by AI, empowered by AI
TL;DR: Progress is surveyed in theoretical, algorithmic, and engineering work toward building AI‐powered intelligent systems to help agents make group decisions based on uncertain preferences; these systems leverage principles, ideas, and methodologies from multiple disciplines.
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Designing Interfaces to Help Stakeholders Comprehend, Navigate, and Manage Algorithmic Trade-Offs.
TL;DR: The interface has implications for the deployment of intelligent algorithms and suggest important directions for future work on predicting criminal defendants' likelihood to re-offend and affected participants' trust in algorithmically aided decision making.
2
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