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FaaS Architecture and Verifiable Fairness for ML Systems

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Manage episode 393667484 series 3474385
Contenido proporcionado por HackerNoon. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente HackerNoon o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.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.

  continue reading

400 episodios

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Manage episode 393667484 series 3474385
Contenido proporcionado por HackerNoon. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente HackerNoon o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.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.

  continue reading

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