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
Fair mapping
TL;DR: A novel pre-processing method based on the transformation of the distribution of protected groups onto a chosen target one, with additional privacy constraints whose objective is to prevent the inference of sensitive attributes is proposed, which preserves the interpretability of data and can be used without defining exactly the sensitive groups.
Journal Article
On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers
TL;DR: This paper proposes a novel model called latent assessment model (LAM) to characterize binary feedback provided by human auditors, and proves that individual and/or group fairness notions are guaranteed as long as the auditor’s intrinsic judgments inherently satisfy the fairness notion at hand, and are relatively similar to the classifier's evaluations.
•Posted Content
Fairness for Whom? Critically reframing fairness with Nash Welfare Product.
TL;DR: This work seeks to reconceptualize and critically frame the existing discourse on fairness by underlining the implicit biases embedded in common understandings of fairness in the literature and how they contrast with its corresponding economic and legal definitions.
An Open Source Replication of a Winning Recidivism Prediction Model.
Giovanni Circo,Andrew P. Wheeler +1 more
TL;DR: This article used a non-linear machine learning model, XGBoost, to balance false positive rates between white and black parolees in recidivism forecasting task and achieved the best performance in the history of the NIMC Recidivism Forecasting Challenge.
Resolving the Disparate Impact of Uncertainty: Affirmative Action vs. Affirmative Information
TL;DR: It is shown that predictive uncertainty often leads classifiers to systematically disadvantage groups with lower-mean outcomes, assigning them smaller true and false positive rates than their higher-mean counterparts, and it is proved that to avoid these error imbalances, individuals in lower- Mean groups must either be over-represented among positive classifications or be assigned more accurate predictions.
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