OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning
arxiv(2024)
摘要
Recently, there has been a surge of explainable AI (XAI) methods driven by
the need for understanding machine learning model behaviors in high-stakes
scenarios. However, properly evaluating the effectiveness of the XAI methods
inevitably requires the involvement of human subjects, and conducting
human-centered benchmarks is challenging in a number of ways: designing and
implementing user studies is complex; numerous design choices in the design
space of user study lead to problems of reproducibility; and running user
studies can be challenging and even daunting for machine learning researchers.
To address these challenges, this paper presents OpenHEXAI, an open-source
framework for human-centered evaluation of XAI methods. OpenHEXAI features (1)
a collection of diverse benchmark datasets, pre-trained models, and post hoc
explanation methods; (2) an easy-to-use web application for user study; (3)
comprehensive evaluation metrics for the effectiveness of post hoc explanation
methods in the context of human-AI decision making tasks; (4) best practice
recommendations of experiment documentation; and (5) convenient tools for power
analysis and cost estimation. OpenHEAXI is the first large-scale
infrastructural effort to facilitate human-centered benchmarks of XAI methods.
It simplifies the design and implementation of user studies for XAI methods,
thus allowing researchers and practitioners to focus on the scientific
questions. Additionally, it enhances reproducibility through standardized
designs. Based on OpenHEXAI, we further conduct a systematic benchmark of four
state-of-the-art post hoc explanation methods and compare their impacts on
human-AI decision making tasks in terms of accuracy, fairness, as well as
users' trust and understanding of the machine learning model.
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