arxiv:2512.06924

XAM: Interactive Explainability for Authorship Attribution Models

Published on Dec 7, 2025

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Abstract

An interactive explainability framework for authorship attribution models that allows users to explore embedding spaces and construct prediction explanations based on writing style features across multiple granularity levels.

AI-generated summary

We present IXAM, an Interactive eXplainability framework for Authorship Attribution Models. Given an authorship attribution (AA) task and an embedding-based AA model, our tool enables users to interactively explore the model's embedding space and construct an explanation of the model's prediction as a set of writing style features at different levels of granularity. Through a user evaluation, we demonstrate the value of our framework compared to predefined stylistic explanations.

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