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

TL;DR

This paper emphasizes the importance of human-centric fairness and transparency in algorithmic decision systems, exploring perceptions, evaluation tools, and developing understandable artifacts to improve trust and fairness.

Contribution

It introduces a comprehensive approach to understanding human perceptions, evaluating transparency tools, and creating human-understandable artifacts for fair decision-making in ADS.

Findings

Insights into decision-subjects' perceptions of algorithmic decisions

Evaluation of transparency tools' effectiveness

Development of human-understandable fairness artifacts

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.