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
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
TL;DR: This paper explicates the various choices and assumptions made---often implicitly---to justify the use of prediction-based decisions and presents a notationally consistent catalogue of fairness definitions from the ML literature to offer a concise reference for thinking through the choices, assumptions, and fairness considerations of Prediction-based decision systems.
208
Human-Centered AI
Hollen Barmer,Rachel Dzombak,Matthew E. Gaston,Vijaykumar Palat,Frank Redner,Carol Smith,Tanisha Smith +6 more
- 02 Sep 2021
TL;DR: Three specific areas of focus to advance human-centered AI are identified: development of tools, processes, and practices to scope and facilitate human-machine teaming, critical and reflective oversight by organizations, teams, and individuals that create and use AI systems, and ongoing deployment.
181
•Book
An Introduction to Ethics in Robotics and AI
Christoph Bartneck,Christoph Lütge,Alan R. Wagner,Sean Welsh +3 more
- 11 Aug 2020
•Posted Content
Algorithmic Fairness in Education.
René F. Kizilcec,Hansol Lee +1 more
TL;DR: This introduction to algorithmic fairness in education draws parallels to prior literature on educational access, bias, and discrimination, and examines core components of algorithmic systems to identify sources of bias and discrimination in the process of developing and deploying these systems.
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
Maria De-Arteaga,Alexey Romanov,Hanna Wallach,Jennifer Chayes,Christian Borgs,Alexandra Chouldechova,Sahin Cem Geyik,Krishnaram Kenthapadi,Adam Tauman Kalai +8 more
- 29 Jan 2019
TL;DR: In this article, the authors present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives.
136
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