ConSim: Measuring Concept-Based Explanations' Effectiveness with Automated Simulatability

Concept-based explanations work by mapping complex model computations to human-understandable concepts. Evaluating such explanations is very difficult, as it includes not only the quality of the induced space of possible concepts but also how effectively the chosen concepts are communicated to users. Existing evaluation metrics often focus solely on the former, neglecting the latter. We introduce an evaluation framework for measuring concept explanations via automated simulatability: a simulator's ability to predict the explained model's outputs based on the provided explanations. This approach accounts for both the concept space and its interpretation in an end-to-end evaluation. Human studies for simulatability are notoriously difficult to enact, particularly at the scale of a wide, comprehensive empirical evaluation (which is the subject of this work). We propose using large language models (LLMs) as simulators to approximate the evaluation and report various analyses to make such approximations reliable. Our method allows for scalable and consistent evaluation across various models and datasets. We report a comprehensive empirical evaluation using this framework and show that LLMs provide consistent rankings of explanation methods. Code available at https://github.com/AnonymousConSim/ConSim.

Natural Example-Based Explainability: a Survey

Explainable Artificial Intelligence (XAI) has become increasingly significant for improving the interpretability and trustworthiness of machine learning models. While saliency maps have stolen the show for the last few years in the XAI field, their ability to reflect models' internal processes has been questioned. Although less in the spotlight, example-based XAI methods have continued to improve. It encompasses methods that use examples as explanations for a machine learning model's predictions. This aligns with the psychological mechanisms of human reasoning and makes example-based explanations natural and intuitive for users to understand. Indeed, humans learn and reason by forming mental representations of concepts based on examples. This paper provides an overview of the state-of-the-art in natural example-based XAI, describing the pros and cons of each approach. A "natural" example simply means that it is directly drawn from the training data without involving any generative process. The exclusion of methods that require generating examples is justified by the need for plausibility which is in some regards required to gain a user's trust. Consequently, this paper will explore the following family of methods: similar examples, counterfactual and semi-factual, influential instances, prototypes, and concepts. In particular, it will compare their semantic definition, their cognitive impact, and added values. We hope it will encourage and facilitate future work on natural example-based XAI.