Research Publications
Chen, C., Liao, M., & Sundar, S. S. (2024). When to explain? Exploring the effects of explanation timing on user perceptions and trust in AI systems. Proceedings of the Second International Symposium on Trustworthy Autonomous Systems (TAS’24), Article No. 10.
Tsai, C. H., Nandy, G., House, D., & Carroll, J. (2024). Ensuring transparency in using ChatGPT for public sentiment analysis. In Proceedings of the 25th Annual International Conference on Digital Government Research (pp. 627-636).
Xu, J., & Ding, M. (2019). Using the Double Transparency of Autonomous Vehicles to Increase Fairness and Social Welfare. Customer Needs and Solutions, 6(1), 26–35.
Zhang, X., Lee, D., & Wang, S. (2024, July 27). Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector. arXiv.Org.
Seo, H., Lee, S., Lee, D., & Xiong, A. (2024). Reliability Matters: Exploring the Effect of AI Explanations on Misinformation Detection with a Warning. Proceedings of the International AAAI Conference on Web and Social Media, 18, 1395–1407.
Liao, Q. V., & Sundar, S. S. (2022). Designing for Responsible Trust in AI Systems: A Communication Perspective. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, 1257–1268.
Badr, Y., & Sharma, R. (2022). Data Transparency and Fairness Analysis of the NYPD Stop-and-Frisk Program. Journal of Data and Information Quality, 14(2), 1–14.
Cho, E., Sundar, S. S., Abdullah, S., & Motalebi, N. (2020). Will deleting history make alexa more trustworthy? effects of privacy and content customization on user experience of smart speakers. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
Dai, E., Zhao, T., Zhu, H., Xu, J., Guo, Z., Liu, H., Tang, J., & Wang, S. (2022). A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability (arXiv:2204.08570). arXiv.
Molina, M. D., & Sundar, S. S. (2022). When AI moderates online content: Effects of human collaboration and interactive transparency on user trust. Journal of Computer-Mediated Communication, 27(4), zmac010.
Rader, E., Cotter, K., & Cho, J. (2018). Explanations as mechanisms for supporting algorithmic transparency. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-13).
Tsai, C. H., & Carroll, J. M. (2022). Logic and Pragmatics in AI Explanation. In International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (pp. 387-396). Springer, Cham.
Xu, J., & Ding, M. (2019). Using the double transparency of autonomous vehicles to increase fairness and social welfare. Customer Needs and Solutions, 6(1), 26-35.
Yen, J., Wang, L., & Gillespie, C. W. (1998). Improving the interpretability of TSK fuzzy models by combining global learning and local learning. IEEE Transactions on Fuzzy Systems, 6(4), 530–537.
Shen, J., DiPaola, D., Ali, S., Sap, M., Park, H. W., & Breazeal, C. (2024). Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study. JMIR Mental Health, 11(1), e62679.
Xuan, Y., Small, E., Sokol, K., Hettiachchi, D., & Sanderson, M. (2024). Comprehension is a double-edged sword: Over-interpreting unspecified information in intelligible machine learning explanations. International Journal of Human-Computer Studies, 103376.
Palazzolo, P., Stahl, B., & Webb, H. (2024, September). Measurable Trust: The Key to Unlocking User Confidence in Black-Box AI. In Proceedings of the Second International Symposium on Trustworthy Autonomous Systems (pp. 1-7).
Coeckelbergh, M. (2020). Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, 26(4), 2051–2068.
Adebayo, J., Muelly, M., Abelson, H., & Kim, B. (2022, January 28). Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. International Conference on Learning Representations.
Anjomshoae, S., Najjar, A., Calvaresi, D., & Främling, K. (2019). Explainable Agents and Robots: Results from a Systematic Literature Review. 1078–1088.
Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY, 35(3), 611–623.
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
Beyond State v Loomis: Artificial intelligence, government algorithmization and accountability | International Journal of Law and Information Technology | Oxford Academic. (n.d.). Retrieved May 23, 2022
Constantinescu, M., Voinea, C., Uszkai, R., & Vică, C. (2021). Understanding responsibility in Responsible AI. Dianoetic virtues and the hard problem of context. Ethics and Information Technology, 23(4), 803–814.
Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer International Publishing.
Ghallab, M. (2019). Responsible AI: Requirements and challenges. AI Perspectives, 1(1), 3.
Givens, A. R., & Morris, M. R. (2020). Centering disability perspectives in algorithmic fairness, accountability, & transparency. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 684–684.
Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: Accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256.
Kang, S. S. (2020). Algorithmic accountability in public administration: The GDPR paradox. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 32–32.
Lima, G., & Cha, M. (2020). Responsible AI and Its Stakeholders (arXiv:2004.11434). arXiv.
Lu, Q., Zhu, L., Xu, X., & Whittle, J. (2022). Responsible-AI-by-Design: A Pattern Collection for Designing Responsible AI Systems (arXiv:2203.00905). arXiv.
Martin, K. (2019). Ethical Implications and Accountability of Algorithms. Journal of Business Ethics: JBE, 160(4), 835–850.
Phillips, P. J., Hahn, C. A., Fontana, P. C., Broniatowski, D. A., & Przybocki, M. A. (2020). Four Principles of Explainable Artificial Intelligence [Preprint].
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing (arXiv:2001.00973). arXiv.
Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2021). Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for Shifting Organizational Practices. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–23.
Ratti, E., & Graves, M. (2022). Explainable machine learning practices: Opening another black box for reliable medical AI. AI and Ethics.
Scantamburlo, T., Cortés, A., & Schacht, M. (2020). Progressing Towards Responsible AI (arXiv:2008.07326). arXiv.
Schiff, D., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2020). Principles to Practices for Responsible AI: Closing the Gap (arXiv:2006.04707). arXiv.
Shneiderman, B. (2021). Responsible AI: Bridging from ethics to practice. Communications of the ACM, 64(8), 32–35.
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. California Management Review, 61(4), 15–42.
Tigard, D. W. (2021). Responsible AI and moral responsibility: A common appreciation. AI and Ethics, 1(2), 113–117.
Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14.
Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 1–18.
Explainable Artificial Intelligence for Cyber Security—Google Books. (n.d.)
Molnar, C. (2020). Interpretable Machine Learning. Lulu.com.