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
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
TL;DR: 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, and the impact on occupation classification of including explicit gender indicators in different semantic representations of online biographies.
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A Framework for Understanding Unintended Consequences of Machine Learning
Harini Suresh,John V. Guttag +1 more
- 28 Jan 2019
TL;DR: This paper provides a framework that partitions sources of downstream harm in machine learning into six distinct categories spanning the data generation and machine learning pipeline, and describes how these issues arise, how they are relevant to particular applications, and how they motivate different solutions.
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Neurosymbolic AI: The 3rd Wave
TL;DR: The insights provided by 20 years of neural-symbolic computing are shown to shed new light onto the increasingly prominent role of trust, safety, interpretability and accountability of AI.
Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies
TL;DR: In a recent report, the Administrative Conference of the United States (ACUS) commissioned a report from researchers at Stanford University and New York University as mentioned in this paper, which offers three cuts at federal agency use of AI:
217
The Promise of Adolescence: Realizing Opportunity for All Youth
Youth Board on Children,Families,Emily P. Backes,Richard J. Bonnie +3 more
- 16 May 2019
TL;DR: The authors examined the neurobiological and socio-behavioral science of adolescent development and outlined how this knowledge can be applied, both to promote adolescent well-being, resilience, and development, and to rectify structural barriers and inequalities in opportunity, enabling all adolescents to flourish.
214
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