AutoFairML: An Automated Middleware for Fairness Auditing in Real-world AI Pipelines

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00389740" target="_blank" >RIV/68407700:21230/25:00389740 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ICDCSW63273.2025.00050" target="_blank" >https://doi.org/10.1109/ICDCSW63273.2025.00050</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDCSW63273.2025.00050" target="_blank" >10.1109/ICDCSW63273.2025.00050</a>

Result language

angličtina

Original language name

AutoFairML: An Automated Middleware for Fairness Auditing in Real-world AI Pipelines

Original language description

The great proliferation of Artificial Intelligence in a wide variety of application domains has produced an imminent need to interpret the output of such models, especially for evaluating their fairness. Such domains may include human resources management for evaluating individual and group fairness, advertising and financial technologies, where fairness can have significant impact. However, existing tools like AIF360 and AIX360, although important for helping practitioners in industrial AI pipelines, have significant shortcomings. Such tools suffer from poor scalability and developers cannot directly benefit from, since there is no middleware available to support and take advantage of modern AI infrastructures. In this work, we present AutoFairML, an automated middleware able to leverage existing tools to detect bias in AI/ML models, examine trade-offs between different measures of fairness, certify their fairness a priori and provide explanations for the models. This represents a significant improvement in the usability and the real-world applicability of specialized fairness and explainability toolkits such as AIF360 and AIX360. Our experimental evaluation using real-world pipelines and data highlights its merits in terms of practicality and efficiency.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings - 2025 IEEE 45th International Conference on Distributed Computing Systems Workshops

ISBN

979-8-3315-1726-7

ISSN

1545-0678

e-ISSN

—

Number of pages

6

Pages from-to

261-266

Publisher name

IEEE Industrial Electronic Society

Place of publication

Vienna

Event location

Glasgov

Event date

Jul 20, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001669747800044