A Human-Centric Assessment Framework for AI

TL;DR

This paper proposes a human-centric assessment framework for AI, inspired by the Turing test, to evaluate AI systems' performance and explainability through expert acceptance rates.

Contribution

It introduces a novel, human-centric evaluation framework for AI explainability, including two instantiations for assessing accuracy and explanation usefulness.

Findings

Framework effectively compares AI and human expert acceptance rates.

Assessment can evaluate both classification accuracy and explanation usefulness.

Demonstrated applicability through two specific instantiations.

Abstract

With the rise of AI systems in real-world applications comes the need for reliable and trustworthy AI. An essential aspect of this are explainable AI systems. However, there is no agreed standard on how explainable AI systems should be assessed. Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accepts or rejects the solutions of an AI system and another domain expert. By comparing the acceptance rates of provided solutions, we can assess how the AI system performs compared to the domain expert, and whether the AI system's explanations (if provided) are human-understandable. This setup -- comparable to the Turing test -- can serve as a framework for a wide range of human-centric AI system assessments. We demonstrate this by presenting two instantiations: (1) an assessment that measures the classification accuracy of a system with the option to incorporate label uncertainties; (2) an assessment where the usefulness of provided explanations is determined in a human-centric manner.