Evaluating the Performance of Fairness Audits in Real-World ML Systemsby@escholar

Evaluating the Performance of Fairness Audits in Real-World ML Systems

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This section delves into the practical implementation and performance analysis of Fairness as a Service (FaaS). Using a medical dataset, the study examines execution times, cryptographic computations, and scalability. Key findings reveal the computational demands of fairness audits, providing real-world insights into FaaS performance and its implications for machine learning systems.

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by EScholar: Electronic Academic Papers for Scholars @escholar.We publish the best academic work (that's too often lost to peer reviews & the TA's desk) to the global tech community
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