A Human-Centric Assessment Framework for AI
- URL: http://arxiv.org/abs/2205.12749v1
- Date: Wed, 25 May 2022 12:59:13 GMT
- Title: A Human-Centric Assessment Framework for AI
- Authors: Sascha Saralajew and Ammar Shaker and Zhao Xu and Kiril Gashteovski
and Bhushan Kotnis and Wiem Ben-Rim and J\"urgen Quittek and Carolin Lawrence
- Abstract summary: 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.
This setup can serve as framework for a wide range of human-centric AI system assessments.
- Score: 11.065260433086024
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the rise of AI systems in real-world applications comes the need for
reliable and trustworthy AI. An important aspect for 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 in comparison to the domain expert, and in turn whether or not the AI
system's explanations (if provided) are human understandable. This setup --
comparable to the Turing test -- can serve as 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.
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