HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Automatic Fairness Testing of Machine Learning Models

Abstract : In recent years, there has been an increased application of machine learning (ML) to decision making systems. This has prompted an urgent need for validating requirements on ML models. Fairness is one such requirement to be ensured in numerous application domains. It specifies a software as “learned” by an ML algorithm to not be biased in the sense of discriminating against some attributes (like gender or age), giving different decisions upon flipping the values of these attributes.In this work, we apply verification-based testing (VBT) to the fairness checking of ML models. Verification-based testing employs verification technology to generate test cases potentially violating the property under interest. For fairness testing, we additionally provide a specification language for the formalization of different fairness requirements. From the ML model under test and fairness specification VBT automatically generates test inputs specific to the specified fairness requirement. The empirical evaluation on several benchmark ML models shows verification-based testing to perform better than existing fairness testing techniques with respect to effectiveness.
Complete list of metadata

https://hal.inria.fr/hal-03239825
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, May 27, 2021 - 4:43:01 PM
Last modification on : Thursday, May 27, 2021 - 4:58:37 PM
Long-term archiving on: : Saturday, August 28, 2021 - 8:00:12 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Arnab Sharma, Heike Wehrheim. Automatic Fairness Testing of Machine Learning Models. 32th IFIP International Conference on Testing Software and Systems (ICTSS), Dec 2020, Naples, Italy. pp.255-271, ⟨10.1007/978-3-030-64881-7_16⟩. ⟨hal-03239825⟩

Share

Metrics

Record views

22