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Privacy-preserving Computation of Fairness for ML Systems: Acknowledgement & References
by EScholar: Electronic Academic Papers for ScholarsJanuary 3rd, 2024
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Fairness as a Service (FaaS) revolutionizes algorithmic fairness audits by preserving privacy without accessing original datasets or model specifics. This paper presents FaaS as a trustworthy framework employing encrypted cryptograms and Zero Knowledge Proofs. Security guarantees, a proof-of-concept implementation, and performance experiments showcase FaaS as a promising avenue for calculating and verifying fairness in AI algorithms, addressing challenges in privacy, trust, and performance.People Mentioned
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This paper is available on arxiv under CC BY 4.0 DEED license.
Authors:
(1) Ehsan Toreini, University of Surrey, UK;
(2) Maryam Mehrnezhad, Royal Holloway University of London;
(3) Aad Van Moorsel, Birmingham University.
Table of Links
Implementation and Performance Analysis
Acknowledgment
The authors in this project have been funded by UK EPSRC grant “FinTrust: Trust Engineering for the Financial Industry” under grant number EP/R033595/1, and UK EPSRC grant “AGENCY: Assuring Citizen Agency in a World with Complex Online Harms” under grant EP/W032481/1 and PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1.
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