Over the past years, there has been an increasing concern regarding the risk of bias and discrimination in algorithmic systems, which received significant attention amongst the research communities. To ensure the system's fairness, various methods and techniques have been developed to assess and mitigate potential biases. Such methods, also known as "Formal Fairness", look at various aspects of the system's advanced reasoning mechanism and outcomes, with techniques ranging from local explanations (at feature level) to visual explanations (saliency maps). Another aspect, equally important, represents the perception of the users regarding the system's fairness. Despite a decision system being provably "Fair", if the users find it difficult to understand how the decisions were made, they will refrain from trusting, accepting, and ultimately using the system altogether. This raised the issue of "Perceived Fairness"which looks at means to reassure users of a system's trustworthiness. In that sense, providing users with some form of explanation on why and how certain outcomes resulted, is highly relevant, especially nowadays as the reasoning mechanisms increase in complexity and computational power. Recent studies suggest a plethora of explanation types. The current work aims to review the recent progress in explaining systems' reasoning and outcome, categorize and present it as a reference for the state-of-the-art fairness-related explanations review.

Recent Studies of XAI - Review

Hu Z. F.;Kuflik T.;
2021

Abstract

Over the past years, there has been an increasing concern regarding the risk of bias and discrimination in algorithmic systems, which received significant attention amongst the research communities. To ensure the system's fairness, various methods and techniques have been developed to assess and mitigate potential biases. Such methods, also known as "Formal Fairness", look at various aspects of the system's advanced reasoning mechanism and outcomes, with techniques ranging from local explanations (at feature level) to visual explanations (saliency maps). Another aspect, equally important, represents the perception of the users regarding the system's fairness. Despite a decision system being provably "Fair", if the users find it difficult to understand how the decisions were made, they will refrain from trusting, accepting, and ultimately using the system altogether. This raised the issue of "Perceived Fairness"which looks at means to reassure users of a system's trustworthiness. In that sense, providing users with some form of explanation on why and how certain outcomes resulted, is highly relevant, especially nowadays as the reasoning mechanisms increase in complexity and computational power. Recent studies suggest a plethora of explanation types. The current work aims to review the recent progress in explaining systems' reasoning and outcome, categorize and present it as a reference for the state-of-the-art fairness-related explanations review.
29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021
nld
2021
UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization
Association for Computing Machinery, Inc
421
431
9781450383677
https://dl.acm.org/doi/10.1145/3450614.3463354
algorithmic transparency; Explainability; Perceived fairness
Hu Z.F.; Kuflik T.; Mocanu I.G.; Najafian S.; Shulner Tal A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1801813
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