BUILDING USER TRUST IN CONVERSATIONAL AI: THE ROLE OF EXPLAINABLE AI IN CHATBOT TRANSPARENCY
Keywords:
Explainable AI (XAI), Chatbot Transparency, LIME , SHAP, Counterfactual Explanations, AI Trust , AccountabilityAbstract
This article explores the application of Explainable AI (XAI) techniques to enhance transparency and trust in chatbot decision-making processes. As chatbots become increasingly sophisticated, understanding their internal reasoning remains a significant challenge. We investigate the implementation of three key XAI methods—LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and counterfactual explanations—in the context of modern chatbot systems. Through a comprehensive analysis involving multiple chatbot models and user studies, we demonstrate the effectiveness of these techniques in providing interpretable insights into chatbot behavior. Our article reveals improvements in user trust and system accountability, while also highlighting challenges in real-time explanation generation and the need for balancing explanation complexity with user comprehension. The article further explores the implications of XAI for chatbot development, deployment, and ethical considerations. By addressing current limitations and proposing future research directions, including advanced XAI techniques for complex language models and the integration of explainability in chatbot learning processes, this research contributes to the ongoing effort to create more transparent, reliable, and user-centric conversational AI systems.
References
A. Følstad and P. B. Brandtzaeg, "Chatbots and the new world of HCI," Interactions, vol. 24, no. 4, pp. 38-42, 2017. [Online]. Available: https://doi.org/10.1145/3085558
J. Weizenbaum, "ELIZA—a computer program for the study of natural language communication between man and machine," Communications of the ACM, vol. 9, no. 1, pp. 36-45, 1966. [Online]. Available: https://doi.org/10.1145/365153.365168
Z. C. Lipton, "The mythos of model interpretability," Queue, vol. 16, no. 3, pp. 31-57, 2018. [Online]. Available: https://doi.org/10.1145/3236386.3241340
M. T. Ribeiro, S. Singh, and C. Guestrin, "'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, 2016, pp. 1135-1144. [Online]. Available: https://doi.org/10.1145/2939672.2939778
R. K. Mothilal, A. Sharma, and C. Tan, "Explaining machine learning classifiers through diverse counterfactual explanations," in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020, pp. 607-617. [Online]. Available: https://doi.org/10.1145/3351095.3372850
S. Anjomshoae, A. Najjar, D. Calvaresi, and K. Främling, "Explainable agents and robots: Results from a systematic literature review," in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019, pp. 1078-1088. [Online]. Available: https://dl.acm.org/doi/10.5555/3306127.3331806
A. Adadi and M. Berrada, "Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)," IEEE Access, vol. 6, pp. 52138-52160, 2018. [Online]. Available: https://doi.org/10.1109/ACCESS.2018.2870052
R. R. Hoffman, S. T. Mueller, G. Klein, and J. Litman, "Metrics for explainable AI: Challenges and prospects," IEEE Access, vol. 7, pp. 136974-137001, 2019. [Online]. Available: https://doi.org/10.48550/arXiv.1812.04608
