FaaS Architecture and Verifiable Fairness for ML Systems
![FaaS Architecture and Verifiable Fairness for ML Systems](https://www.podcastworld.io/podcast-images/tech-stories-tech-brief-by-hackernoon-uoasrcua.webp)
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.