ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models

no code implementations • 30 Dec 2022 • Arkadipta De, Satya Swaroop Gudipudi, Sourab Panchanan, Maunendra Sankar Desarkar

In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective.

Binary Classification Fairness +1