Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI

Şadi Evren Şeker; Zümra Arslanhan

Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI

Abstract

The increasing complexity of machine learning models has created a critical need for explainable artificial intelligence (XAI). While prominent post-hoc explanation frameworks like LIME and SHAP have provided foundational tools for model-agnostic interpretation, they often produce atomic feature-attribution scores that lack contextual realism and fail to provide holistic, actionable narratives. This paper introduces Hermai, a novel open-source Python framework designed to bridge this gap. Hermai is founded on the philosophical principles of hermeneutics, positing that true understanding arises from a dialogue between the parts (local predictions) and the whole (global model behavior). The framework makes three primary contributions: (1) a context-aware perturbation engine that generates plausible data points by respecting feature correlations, moving beyond the feature independence assumption of simpler methods; (2) a dual-mode explanatory system that generates both local, counterfactual-driven narratives for single predictions and general, rule-based explanations of the model's overall logic using surrogate models; and (3) the synthesis of these elements into human-readable, narrative explanations. We demonstrate Hermai's architecture, its practical application on a classic dataset, and argue that its hermeneutic approach offers a more intuitive, coherent, and trustworthy method for interpreting complex models.

Keywords

References

[1] Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052
[2] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778
[3] Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30 (NIPS 2017).
[4] Molnar, C. (2020). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Lulu.com.
[5] Gadamer, H.-G. (1989). Truth and Method (2nd, rev. ed.). (J. Weinsheimer & D. G. Marshall, Trans.). Sheed & Ward.
[6] European Union. (2024). Artificial Intelligence Act. Official Journal of the European Union.
[7] Sundararajan, M., Taly, A., & Yan, Q. (2017). Axiomatic Attribution for Deep Networks. Proceedings of the 34th International Conference on Machine Learning, 70, 3319-3328.
[8] Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841-887.

[9] Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37), eaay7120. https://doi.org/10.1126/scirobotics.aay7120
[10] Sadi Evren SEKER, “Computerized Argument Delphi Technique”, IEEE Access, 2015, v. 3, pp. 368 – 380 DOI: 10.1109/ACCESS.2015.2424703
[11] R. K. Mothilal, A. Sharma, and C. Tan. 2020. Explaining machine learning classifiers through diverse counterfactual explanations. Proceedings of FAT, 607–617.
[12] B. Ustun, A. Spangher, and Y. Liu. 2019. Actionable recourse in linear classification. Proceedings of FAT, 10–18.
[13] A. Dhurandhar, P.-Y. Chen, R. Luss, C.-C. Tu, P.-S. Ting, K. Shanmugam, and P. Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. NeurIPS, 592–603.
[14] B. Goodman and S. Flaxman. 2017. European Union regulations on algorithmic decision-making and a “right to explanation.” AI Magazine, 38(3), 50–57.
[15] K. Simonyan, A. Vedaldi, and A. Zisserman. 2014. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034
[16] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. 2017. Grad-CAM: Visual explanations from deep networks via gradient-based localization. ICCV, 618–626.
[17] Kim, J., Maathuis, H., & Sent, D. (2024). Human-centered evaluation of explainable AI applications: A systematic review. Frontiers in Artificial Intelligence, 7, 1456486.
[18] Ehsan, U., Watkins, E. A., Wintersberger, P., et al. (2024). Human-Centered Explainable AI (HCXAI): Reloading Explainability in the Era of Large Language Models. Proceedings of CHI.
[19] Bobek, S., et al. (2025). User-centric evaluation of explainability of AI: A comprehensive empirical study. Artificial Intelligence.
[20] M. Miró-Nicolau, A. Jaume-i-Capó, and G. Moyà-Alcover. 2024. A comprehensive study on fidelity metrics for Explainable AI. arXiv preprint arXiv:2401.10640.
[21] T. Fel, E. Štrumbelj, and D. Benčina. 2022. How good is your explanation? Algorithmic stability measures for post hoc explanation methods. IEEE WACV, 1285–1294.
[22] C. Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215
[23] F. Doshi-Velez and B. Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608
[24] A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, et al. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
[25] Y. Rong, T. Leemann, T.-T. Nguyen, L. Fiedler, P. Qian, V. Unhelkar, T. Seidel, G. Kasneci, and E. Kasneci. 2022. Towards human-centered explainable AI: A survey of user studies for model explanations. arXiv preprint arXiv:2210.11584
[26] Selbst, A. D., & Powles, J. (2017). Meaningful information and the right to explanation. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 1-13. https://doi.org/10.1145/3155695.3156621
[27] D. Dua and C. Graff. 2019. UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences.
[28] L. Breiman. 2001. Random Forests. Machine Learning, 45(1), 5–32.
[29] P. Probst, A. Boulesteix, and B. Bischl. 2019. Tunability: Importance of hyperparameters of machine learning algorithms. Journal of Machine Learning Research, 20(53), 1–32.
[30] M. T. Ribeiro, S. Singh, and C. Guestrin. 2016. “Why Should I Trust You?” Explaining the Predictions of Any Classifier. Proceedings of KDD.
[31] S. M. Lundberg and S.-I. Lee. 2017. A Unified Approach to Interpreting Model Predictions. NeurIPS.
[32] A. Alvarez-Melis and T. Jaakkola. 2018. On the robustness of interpretability methods. Proceedings of ICML Workshop.
[33] T. Fel, E. Štrumbelj, and D. Benčina. 2022. Quantifying stability in explanation methods. Artificial Intelligence, 304.
[34] M. Molnar. 2022. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Leanpub.
[35] T. Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38.

Details

Primary Language

English

Subjects

Knowledge Representation and Reasoning

Journal Section

Research Article

Authors

Şadi Evren Şeker ^*
0000-0002-7323-3695
Türkiye

Zümra Arslanhan
Türkiye

Publication Date

May 3, 2026

Submission Date

October 3, 2025

Acceptance Date

April 24, 2026

Published in Issue

Year 2026 Volume: 6 Number: 1

IZ

https://izlik.org/JA79HS46RD

Cite

RIS / Bibtex

APA

Şeker, Ş. E., & Arslanhan, Z. (2026). Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI. Artificial Intelligence Theory and Applications, 6(1), 56-74. https://izlik.org/JA79HS46RD

AMA

1.Şeker ŞE, Arslanhan Z. Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI. AITA. 2026;6(1):56-74. https://izlik.org/JA79HS46RD

Chicago

Şeker, Şadi Evren, and Zümra Arslanhan. 2026. “Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI”. Artificial Intelligence Theory and Applications 6 (1): 56-74. https://izlik.org/JA79HS46RD.

EndNote

Şeker ŞE, Arslanhan Z (May 1, 2026) Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI. Artificial Intelligence Theory and Applications 6 1 56–74.

IEEE

[1]Ş. E. Şeker and Z. Arslanhan, “Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI”, AITA, vol. 6, no. 1, pp. 56–74, May 2026, [Online]. Available: https://izlik.org/JA79HS46RD

ISNAD

Şeker, Şadi Evren - Arslanhan, Zümra. “Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI”. Artificial Intelligence Theory and Applications 6/1 (May 1, 2026): 56-74. https://izlik.org/JA79HS46RD.

JAMA

1.Şeker ŞE, Arslanhan Z. Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI. AITA. 2026;6:56–74.

MLA

Şeker, Şadi Evren, and Zümra Arslanhan. “Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI”. Artificial Intelligence Theory and Applications, vol. 6, no. 1, May 2026, pp. 56-74, https://izlik.org/JA79HS46RD.

Vancouver

1.Şadi Evren Şeker, Zümra Arslanhan. Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI. AITA [Internet]. 2026 May 1;6(1):56-74. Available from: https://izlik.org/JA79HS46RD