))
FaaS Architecture and Verifiable Fairness for ML Systems
Manage episode 393667484 series 3474385
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
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.
397 episodes
Share
