Across three datasets, five language models, and six post-hoc attribution methods, explanation faithfulness, robustness, and complexity scores differ significantly between male and female inputs in a large majority of 5,040 tested configurations.
Trends in Explainable AI (XAI) Literature
1 Pith paper cite this work. Polarity classification is still indexing.
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Pith paper citing it
abstract
The XAI literature is decentralized, both in terminology and in publication venues, but recent years saw the community converge around keywords that make it possible to more reliably discover papers automatically. We use keyword search using the SemanticScholar API and manual curation to collect a well-formatted and reasonably comprehensive set of 5199 XAI papers, available at https://github.com/alonjacovi/XAI-Scholar . We use this collection to clarify and visualize trends about the size and scope of the literature, citation trends, cross-field trends, and collaboration trends. Overall, XAI is becoming increasingly multidisciplinary, with relative growth in papers belonging to increasingly diverse (non-CS) scientific fields, increasing cross-field collaborative authorship, increasing cross-field citation activity. The collection can additionally be used as a paper discovery engine, by retrieving XAI literature which is cited according to specific constraints (for example, papers that are influential outside of their field, or influential to non-XAI research).
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
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Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Across three datasets, five language models, and six post-hoc attribution methods, explanation faithfulness, robustness, and complexity scores differ significantly between male and female inputs in a large majority of 5,040 tested configurations.