The Quarrel of Local Post-hoc Explainers for Moral Values Classification in Natural Language Processing

@talk{quarrelextraamas2023,
    abstract = {Although popular and effective, large language models (LLM) are characterised by a performance vs. transparency trade-off that hinders their applicability to sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations recently proposed by the XAI community. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, mainly for the lack of a general metric to measure their benefits. We compare state-of-the-art local post-hoc explanation mechanisms for models trained over moral value classification tasks based on a measure of correlation. By relying on a novel framework for comparing global impact scores, our experiments show how most local post-hoc explainers are loosely correlated, and highlight huge discrepancies in their results, their ``quarrel'' about explanations. Finally, we compare the impact scores distribution obtained from each local post-hoc explainer with human-made dictionaries, and point out that there is no correlation between explanation outputs and the concepts humans consider as salient.},
    address = {London, UK},
    author = {Agiollo, Andrea and Luciano C. Siebert and Pradeep K. Murukannaiah and Omicini, Andrea},
    date = {2023-05-29},
    language = {en},
    month = may,
    sort = {talk},
    speaker = {Agiollo, Andrea},
    title = {The Quarrel of Local Post-hoc Explainers for Moral Values Classification in Natural Language Processing},
    type = {Talk},
    url = {https://github.com/AndAgio/slides-extraamas-2023},
    year = 2023
}