fairness-as-a-service

This story was originally published on HackerNoon at: https://hackernoon.com/privacy-preserving-computation-of-fairness-for-ml-systems-acknowledgement-and-references.
Discover Fairness as a Service (FaaS), an architecture and protocol ensuring algorithmic fairness without exposing the original dataset or model details.
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This story was written by: @ashumerie. Learn more about this writer by checking @ashumerie's about page, and for more stories, please visit hackernoon.com.

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.

This story was originally published on HackerNoon at: https://hackernoon.com/faas-architecture-and-verifiable-fairness-for-ml-systems.
Discover the robust architecture of Fairness as a Service (FaaS), a groundbreaking system for trustworthy fairness audits in machine learning.
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #ml-systems, #ml-fairness, #fairness-as-a-service, #fair-machine-learning, #fairness-in-ai, #faas-architecture, #fairness-computation, #hackernoon-top-story, #hackernoon-es, #hackernoon-hi, #hackernoon-zh, #hackernoon-fr, #hackernoon-bn, #hackernoon-ru, #hackernoon-vi, #hackernoon-pt, #hackernoon-ja, #hackernoon-de, #hackernoon-ko, #hackernoon-tr, and more.

This story was written by: @escholar. Learn more about this writer by checking @escholar's about page, and for more stories, please visit hackernoon.com.

This section unfolds the architecture of Fairness as a Service (FaaS), a revolutionary system for ensuring trust in fairness audits within machine learning. The discussion encompasses the threat model, protocol overview, and the essential phases: setup, cryptogram generation, and fairness evaluation. FaaS introduces a robust approach, incorporating cryptographic proofs and verifiable steps, offering a secure foundation for fair evaluations in the ML landscape.