fairness-in-ai

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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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.

This story was originally published on HackerNoon at: https://hackernoon.com/fairness-in-ai-navigating-complex-ethical-ai-dilemmas-with-beena-ammanath.
Trustworthy AI aims to provide a holistic framework to identify Important questions to ask when developing or using AI
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This story was written by: @linked_do. Learn more about this writer by checking @linked_do's about page, and for more stories, please visit hackernoon.com.

Trustworthy AI aims to provide a holistic framework to identify Important questions to ask when developing or using AI. Trustworthy AI should be fair and impartial, robust and reliable, transparent, explainable, secure, safe, accountable, responsible and with privacy.