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
The Fairness in Algorithmic Fairness
TL;DR: This article argues that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome’s account of fairness as the proportional satisfaction of claims.
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Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection
Soumyajit Gupta,Sooyong Lee,Maria De-Arteaga,M. Lease +3 more
- 14 Feb 2023
TL;DR: This paper propose a multi-task learning approach for toxicity detection, where only training examples relevant to the given demographic group are considered by the loss function. But, their method requires labels for all tasks to be present for every data point, leading to disparate performance.
•Posted Content
A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization.
TL;DR: It is shown that Fairband can efficiently navigate the fairness-accuracy trade-off through hyperparameter optimization, and consistently finds configurations attaining substantially improved fairness at a comparatively small decrease in predictive accuracy.
People Perceive Algorithmic Assessments as Less Fair and Trustworthy Than Identical Human Assessments
Lillio Mok,Sasha Nanda,Ashton Anderson +2 more
- 28 Sep 2023
TL;DR: The results illustrate that who makes risk assessments can influence perceptions of how acceptable those assessments are - even if they are identically accurate and identically biased against subgroups.
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Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior-Change Interventions at Scale
Sandra Matz,Emorie D Beck,Olivia E. Atherton,Mike White,John F. Rauthmann,Dan Mroczek,Min-Hee Kim,Tim Bogg +7 more
TL;DR: Personality science in the digital age holds promise for personalized behavior-change interventions at scale. A classification system for psychological targeting is proposed to facilitate research and application.
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