XAM: Interactive Explainability for Authorship Attribution Models
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.
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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