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

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Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14127))

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.

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Notes

  1. 1.

    https://github.com/cbaziotis/ekphrasis.

  2. 2.

    https://pypi.org/project/emoji/.

  3. 3.

    https://github.com/suinleelab/path_explain.

  4. 4.

    https://github.com/slundberg/shap.

  5. 5.

    https://github.com/marcotcr/lime.

  6. 6.

    https://github.com/huggingface.

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Authors and Affiliations

  1. Dipartimento di Informatica – Scienza e Ingegneria (DISI), Alma Mater Studiorum—Università di Bologna, Cesena, Italy

    Andrea Agiollo & Andrea Omicini

  2. Delft University of Technology, Delft, The Netherlands

    Luciano Cavalcante Siebert & Pradeep Kumar Murukannaiah

Authors
  1. Andrea Agiollo
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  2. Luciano Cavalcante Siebert
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  3. Pradeep Kumar Murukannaiah
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  4. Andrea Omicini
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Corresponding author

Correspondence to Andrea Agiollo .

Editor information

Editors and Affiliations

  1. University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Davide Calvaresi

  2. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Amro Najjar

  3. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Andrea Omicini

  4. Ozyegin University, Istanbul, Türkiye

    Reyhan Aydogan

  5. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Rachele Carli

  6. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Giovanni Ciatto

  7. Université de Technologie de Belfort-Montbéliard, Belfort Cedex, France

    Yazan Mualla

  8. Umeå University, Umeå, Sweden

    Kary Främling

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Agiollo, A., Cavalcante Siebert, L., Murukannaiah, P.K., Omicini, A. (2023). The Quarrel of Local Post-hoc Explainers for Moral Values Classification in Natural Language Processing. In: Calvaresi, D., et al. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2023. Lecture Notes in Computer Science(), vol 14127. Springer, Cham. https://doi.org/10.1007/978-3-031-40878-6_6

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