Research Publications
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.
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.