Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

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.

References

  1. Abnar, S., Zuidema, W.: Quantifying attention flow in transformers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4190–4197. Association for Computational Linguistics, July 2020. https://doi.org/10.18653/v1/2020.acl-main.385

  2. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018). https://doi.org/10.1109/ACCESS.2018.2870052

    Article  Google Scholar 

  3. Agiollo, A., Ciatto, G., Omicini, A.: Graph neural networks as the copula mundi between logic and machine learning: a roadmap. In: Calegari, R., Ciatto, G., Denti, E., Omicini, A., Sartor, G. (eds.) WOA 2021–22nd Workshop “From Objects to Agents”. CEUR Workshop Proceedings, vol. 2963, pp. 98–115. Sun SITE Central Europe, RWTH Aachen University, October 2021. http://ceur-ws.org/Vol-2963/paper18.pdf, 22nd Workshop “From Objects to Agents” (WOA 2021), Bologna, Italy, 1–3 September 2021. Proceedings

  4. Agiollo, A., Ciatto, G., Omicini, A.: Shallow2Deep: restraining neural networks opacity through neural architecture search. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds.) Explainable and Transparent AI and Multi-agent Systems. Third International Workshop, EXTRAAMAS 2021. LNCS, vol. 12688, pp. 63–82. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-82017-6_5

  5. Agiollo, A., Omicini, A.: Load classification: a case study for applying neural networks in hyper-constrained embedded devices. Appl. Sci. 11(24) (2021). https://doi.org/10.3390/app112411957, https://www.mdpi.com/2076-3417/11/24/11957, Special Issue “Artificial Intelligence and Data Engineering in Engineering Applications”

  6. Agiollo, A., Omicini, A.: GNN2GNN: graph neural networks to generate neural networks. In: Cussens, J., Zhang, K. (eds.) Uncertainty in Artificial Intelligence. Proceedings of Machine Learning Research, vol. 180, pp. 32–42. ML Research Press, August 2022. https://proceedings.mlr.press/v180/agiollo22a.html, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022, 1–5 August 2022, Eindhoven, The Netherlands

  7. Agiollo, A., Rafanelli, A., Omicini, A.: Towards quality-of-service metrics for symbolic knowledge injection. In: Ferrando, A., Mascardi, V. (eds.) WOA 2022–23rd Workshop “From Objects to Agents”, CEUR Workshop Proceedings, vol. 3261, pp. 30–47. Sun SITE Central Europe, RWTH Aachen University, November 2022. http://ceur-ws.org/Vol-3261/paper3.pdf

  8. Ali, A., Schnake, T., Eberle, O., Montavon, G., Müller, K.R., Wolf, L.: XAI for transformers: better explanations through conservative propagation. In: International Conference on Machine Learning, pp. 435–451. PMLR (2022). https://proceedings.mlr.press/v162/ali22a.html

  9. Alshomary, M., Baff, R.E., Gurcke, T., Wachsmuth, H.: The moral debater: a study on the computational generation of morally framed arguments. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8782–8797. Association for Computational Linguistics, Dublin, Ireland, May 2022. https://doi.org/10.18653/v1/2022.acl-long.601

  10. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS ONE 10(7), e0130140 (2015). https://doi.org/10.1371/journal.pone.0130140

  11. Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 610–623 (2021). https://doi.org/10.1145/3442188.3445922

  12. Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901 (2020). https://dl.acm.org/doi/abs/10.5555/3495724.3495883

  13. Buhrmester, V., Münch, D., Arens, M.: Analysis of explainers of black box deep neural networks for computer vision: a survey. Mach. Learn. Knowl. Extr. 3(4), 966–989 (2021). https://doi.org/10.3390/make3040048

    Article  Google Scholar 

  14. Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., Sen, P.: A survey of the state of explainable AI for natural language processing. In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pp. 447–459. Association for Computational Linguistics, Suzhou, China, December 2020. https://aclanthology.org/2020.aacl-main.46

  15. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, MN, USA, June 2019. https://doi.org/10.18653/v1/N19-1423

  16. Främling, K., Westberg, M., Jullum, M., Madhikermi, M., Malhi, A.: Comparison of contextual importance and utility with LIME and Shapley values. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds.) Explainable and Transparent AI and Multi-agent Systems - Third International Workshop, EXTRAAMAS 2021. LNCS, vol. 12688, pp. 39–54. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-82017-6_3

  17. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 1–42 (2018). https://doi.org/10.1145/3236009

    Article  Google Scholar 

  18. Hailesilassie, T.: Rule extraction algorithm for deep neural networks: a review. Int. J. Comput. Sci. Inf. Secur. 14(7), 376–381 (2016). https://www.academia.edu/28181177/Rule_Extraction_Algorithm_for_Deep_Neural_Networks_A_Review

  19. Hao, T., Li, X., He, Y., Wang, F.L., Qu, Y.: Recent progress in leveraging deep learning methods for question answering. Neural Comput. Appl. 34(4), 2765–2783 (2022). https://doi.org/10.1007/s00521-021-06748-3

    Article  Google Scholar 

  20. Hoover, J., et al.: Moral foundations Twitter corpus: a collection of 35k tweets annotated for moral sentiment. Soc. Psychol. Pers. Sci. 11(8), 1057–1071 (2020). https://doi.org/10.1177/194855061987662

    Article  Google Scholar 

  21. Hopp, F.R., Fisher, J.T., Cornell, D., Huskey, R., Weber, R.: The extended moral foundations dictionary (eMFD): development and applications of a crowd-sourced approach to extracting moral intuitions from text. Behav. Res. Methods 53, 232–246 (2021). https://doi.org/10.3758/s13428-020-01433-0

    Article  Google Scholar 

  22. Ibrahim, M., Louie, M., Modarres, C., Paisley, J.: Global explanations of neural networks: mapping the landscape of predictions. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 279–287 (2019). https://doi.org/10.1145/3306618.3314230

  23. Jaume, G., et al.: Quantifying explainers of graph neural networks in computational pathology. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual, 19–25 June 2021, pp. 8106–8116. Computer Vision Foundation/IEEE (2021). https://doi.org/10.1109/CVPR46437.2021.00801

  24. Kiesel, J., Alshomary, M., Handke, N., Cai, X., Wachsmuth, H., Stein, B.: Identifying the human values behind arguments. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 4459–4471 (2022). https://doi.org/10.18653/v1/2022.acl-long.306

  25. Kindermans, P.J., et al.: The (un)reliability of saliency methods. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L., Müller, K.R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 267–280. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28954-6_14

  26. Kokalj, E., Škrlj, B., Lavrač, N., Pollak, S., Robnik-Šikonja, M.: BERT meets Shapley: extending SHAP explanations to transformer-based classifiers. In: Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, pp. 16–21 (2021)

    Google Scholar 

  27. Liscio, E., et al.: What does a text classifier learn about morality? An explainable method for cross-domain comparison of moral rhetoric. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, pp. 1–12, Toronto (2023, to appear)

    Google Scholar 

  28. Liscio, E., Dondera, A., Geadau, A., Jonker, C., Murukannaiah, P.: Cross-domain classification of moral values. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 2727–2745. Association for Computational Linguistics, Seattle, United States, July 2022. https://doi.org/10.18653/v1/2022.findings-naacl.209

  29. Liu, G., et al.: Medical-VLBERT: medical visual language BERT for COVID-19 CT report generation with alternate learning. IEEE Trans. Neural Netw. Learn. Syst. 32(9), 3786–3797 (2021). https://doi.org/10.1109/TNNLS.2021.3099165

    Article  Google Scholar 

  30. Loh, W.Y.: Fifty years of classification and regression trees. Int. Stat. Rev. 82(3), 329–348 (2014). https://doi.org/10.1111/insr.12016

    Article  MathSciNet  MATH  Google Scholar 

  31. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

  32. Luo, S., Ivison, H., Han, C., Poon, J.: Local interpretations for explainable natural language processing: a survey. arXiv preprint arXiv:2103.11072 (2021)

  33. Madsen, A., Reddy, S., Chandar, S.: Post-hoc interpretability for neural NLP: a survey. ACM Comput. Surv. 55(8), 1–42 (2022). https://doi.org/10.1145/3546577

    Article  Google Scholar 

  34. Otter, D.W., Medina, J.R., Kalita, J.K.: A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Netw. Learn. Syst. 32(2), 604–624 (2021). https://doi.org/10.1109/TNNLS.2020.2979670

    Article  MathSciNet  Google Scholar 

  35. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485–5551 (2020). https://dl.acm.org/doi/abs/10.5555/3455716.3455856

  36. Ramachandran, D., Parvathi, R.: Analysis of Twitter specific preprocessing technique for tweets. Procedia Comput. Sci. 165, 245–251 (2019). https://doi.org/10.1016/j.procs.2020.01.083

    Article  Google Scholar 

  37. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016). https://doi.org/10.18653/v1/N16-3020

  38. Samek, W., Montavon, G., Lapuschkin, S., Anders, C.J., Müller, K.R.: Explaining deep neural networks and beyond: a review of methods and applications. Proc. IEEE 109(3), 247–278 (2021). https://doi.org/10.1109/JPROC.2021.3060483

    Article  Google Scholar 

  39. Sheikhalishahi, S., et al.: Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med. Inform. 7(2), e12239 (2019). https://doi.org/10.2196/12239

    Article  Google Scholar 

  40. Stahlberg, F.: Neural machine translation: a review. J. Artif. Intell. Res. 69, 343–418 (2020). https://doi.org/10.1613/jair.1.12007

    Article  MathSciNet  Google Scholar 

  41. Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Precup, D., Teh, Y.W. (eds.) International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 3319–3328. PMLR, August 2017. https://proceedings.mlr.press/v70/sundararajan17a.html

  42. Tay, Y., Bahri, D., Metzler, D., Juan, D.C., Zhao, Z., Zheng, C.: Synthesizer: rethinking self-attention for transformer models. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 10183–10192. PMLR, July 2021. https://proceedings.mlr.press/v139/tay21a.html

  43. Warstadt, A., Singh, A., Bowman, S.R.: Neural network acceptability judgments. Trans. Assoc. Comput. Linguist. 7, 625–641 (2019). https://doi.org/10.1162/tacl_a_00290

    Article  Google Scholar 

  44. Wu, Z., Nguyen, T.S., Ong, D.C.: Structured self-attention weights encode semantics in sentiment analysis. In: Proceedings of the Third Blackbox NLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pp. 255–264. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.blackboxnlp-1.24

  45. Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdisc. Rev. Data Mining Knowl. Discov. 8(4) (2018). https://doi.org/10.1002/widm.1253

  46. Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018). https://proceedings.neurips.cc/paper_files/paper/2018/file/f2925f97bc13ad2852a7a551802feea0-Paper.pdf

  47. Zini, J.E., Awad, M.: On the explainability of natural language processing deep models. ACM Comput. Surv. 55(5), 1–31 (2022). https://doi.org/10.1145/3529755

    Article  Google Scholar 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luciano Cavalcante Siebert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pradeep Kumar Murukannaiah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrea Omicini
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-40878-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40877-9

  • Online ISBN: 978-3-031-40878-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation