Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability

• URL: http://arxiv.org/abs/2410.20890v1
• Date: Mon, 28 Oct 2024 10:18:07 GMT
• Title: Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability
• Authors: Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen, Magda Gregorova,
• Abstract summary: We propose a novel probabilistic framework for local example-based explanations. Our aim is to facilitate communication, foster rigor and transparency, and improve the quality of peer discussion and research progress.
• Score: 42.50219822975012
• License:
• Abstract: Recently, several methods have leveraged deep generative modeling to produce example-based explanations of decision algorithms for high-dimensional input data. Despite promising results, a disconnect exists between these methods and the classical explainability literature, which focuses on lower-dimensional data with semantically meaningful features. This conceptual and communication gap leads to misunderstandings and misalignments in goals and expectations. In this paper, we bridge this gap by proposing a novel probabilistic framework for local example-based explanations. Our framework integrates the critical characteristics of classical local explanation desiderata while being amenable to high-dimensional data and their modeling through deep generative models. Our aim is to facilitate communication, foster rigor and transparency, and improve the quality of peer discussion and research progress.

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