FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software

Data-driven software solutions have significantly been used in critical domains with significant socio-economic, legal, and ethical implications. The rapid adoptions of data-driven solutions, however, pose major threats to the trustworthiness of automated decision-support software. A diminished unde...

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Bibliographic Details
Published in:Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) pp. 25 - 28
Main Authors: Yu, Normen, Carreon, Luciana, Tan, Gang, Tizpaz-Niari, Saeid
Format: Conference Proceeding
Language:English
Published: IEEE 27.04.2025
Subjects:
ai fairness
Data models
Data visualization
Debugging
Ethics
fairness mitigation
Law
Logic
Socioeconomics
Software
Software engineering
Surveys
toolkit
ISSN:2574-1934
Online Access:Get full text
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Summary:Data-driven software solutions have significantly been used in critical domains with significant socio-economic, legal, and ethical implications. The rapid adoptions of data-driven solutions, however, pose major threats to the trustworthiness of automated decision-support software. A diminished understanding of the solution by the developer and historical/current biases in the data sets are primary challenges. To aid data-driven software developers and end-users, we present FairLay-ML, a debugging tool to test and explain the fairness implications of data-driven solutions. FairLay-ML visualizes the logic of datasets, trained models, and decisions for a given data point. In addition, it trains various models with varying fairness-accuracy tradeoffs. Crucially, FairLay-ML incorporates counterfactual fairness testing that finds bugs beyond the development datasets. We conducted two studies through FairLay-ML that allowed us to measure false positives/negatives in prevalent counterfactual testing and understand the human perception of counterfactual test cases in a class survey. FairLay-ML and its benchmarks are publicly available at https://github.com/Pennswood/FairLay-ML. The live version of the tool is available at https://fairlayml-v2.streamlit.app/. We provide a video demo of the tool at https://youtu.be/wNI9UWkywVU?t=133.
ISSN:2574-1934
DOI:10.1109/ICSE-Companion66252.2025.00016