A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

• URL: http://arxiv.org/abs/2205.00033v1
• Date: Fri, 29 Apr 2022 18:31:04 GMT
• Title: A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making
• Authors: Jakob Schoeffer
• Abstract summary: Automated decision systems (ADS) are increasingly used for consequential decision-making. Non-transparent systems are prone to yield unfair outcomes because their sanity is challenging to assess and calibrate. I aim to make the following three main contributions through my doctoral thesis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield unfair outcomes because their sanity is challenging to assess and calibrate in the first place -- which is particularly worrisome for human decision-subjects. Based on this observation and building upon existing work, I aim to make the following three main contributions through my doctoral thesis: (a) understand how (potential) decision-subjects perceive algorithmic decisions (with varying degrees of transparency of the underlying ADS), as compared to similar decisions made by humans; (b) evaluate different tools for transparent decision-making with respect to their effectiveness in enabling people to appropriately assess the quality and fairness of ADS; and (c) develop human-understandable technical artifacts for fair automated decision-making. Over the course of the first half of my PhD program, I have already addressed substantial pieces of (a) and (c), whereas (b) will be the major focus of the second half.

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