Privacy-preserving Computation of Fairness for ML Systems: Acknowledgement & Referencesby@escholar
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Privacy-preserving Computation of Fairness for ML Systems: Acknowledgement & References

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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.

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EScholar: Electronic Academic Papers for Scholars

EScholar: Electronic Academic Papers for Scholars

@escholar

We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community

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EScholar: Electronic Academic Papers for Scholars HackerNoon profile picture
EScholar: Electronic Academic Papers for Scholars@escholar
We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community

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