Research Publications
Zhang, X., Tang, X., Liu, H., Wu, Z., He, Q., Lee, D., & Wang, S. (2024). Divide-Verify-Refine: Aligning LLM Responses with Complex Instructions. arXiv preprint arXiv:2410.12207.
Chen, C., Lee, S., Jang, E., & Sundar, S. S. (2024). Is Your Prompt Detailed Enough? Exploring the Effects of Prompt Coaching on Users' Perceptions, Engagement, and Trust in Text-to-Image Generative AI Tools. In Proceedings of the Second International Symposium on Trustworthy Autonomous Systems (pp. 1-12).
Chen, C., Liao, M., Walther, J. B., & Sundar, S. S. (2024). When an AI doctor gets personal: The effects of social and medical individuation in encounters with human and AI doctors. Communication Research, 51(7), 747-781.
Wu, C., Wang, X., Carroll, J., & Rajtmajer, S. (2024, May). Reacting to Generative AI: Insights from Student and Faculty Discussions on Reddit. In Proceedings of the 16th ACM Web Science Conference (pp. 103-113).
Holster, J. (2024). Augmenting Music Education through AI: Practical Applications of ChatGPT. Music Educators Journal, 110(4), 36-42.
Zhang, H., Wu, C., Xie, J., Rubino, F., Graver, S., Kim, C., Carroll, J.M., & Cai, J. (2024). When Qualitative Research Meets Large Language Model: Exploring the Potential of QualiGPT as a Tool for Qualitative Coding. arXiv preprint arXiv:2407.14925.
Huang, S. H., & Huang, T.-H. “Kenneth.” (2024, May). On Replacing Humans with Large Language Models in Voice-Based Human-in-the-Loop Systems. In Proceedings of the AAAI Symposium Series, 3(1), 45-49.
Wu, S., Oltramari, A., Francis, J., Giles, C. L., & Ritter, F. E. (2024). Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making. arXiv preprint arXiv:2408.09176.
Yu, R., Lee, S., Xie, J., Billah, S. M., & Carroll, J. M. (2024). Human–AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era. Future Internet, 16(7), 254.
Gupta, V., Belland, B. R., Billups, A., & Passonneau, R. J. (2023). AI for Coding Education Meta-analyses: An Open-Science Approach that Combines Human and Machine Intelligence. In T. Schlippe, E. C. K. Cheng, & T. Wang (Eds.), Artificial Intelligence in Education Technologies: New Development and Innovative Practices (pp. 14–29). Springer Nature.
Carroll, J. M. (2022). Why should humans trust AI? Interactions, 29(4), 73–77.
Cotter, K. (2023). “Shadowbanning is not a thing”: Black box gaslighting and the power to independently know and credibly critique algorithms. Information, Communication & Society, 26(6), 1226–1243.
You, Y., Kou, Y., Ding, X., & Gui, X. (2021). The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–16.
Uchendu, A., Lee, J., Shen, H., Le, T., Huang, T.-H. “Kenneth,” & Lee, D. (2023). Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts? Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 11(1), Article 1.
Chakravorti, T., Singh, V., Rajtmajer, S., McLaughlin, M., Fraleigh, R., Griffin, C., Kwasnica, A., Pennock, D., & Giles, C. L. (2022). Artificial prediction markets present a novel opportunity for human-AI collaboration (arXiv:2211.16590). arXiv.
Guo, X., Huang, K., Liu, J., Fan, W., Vélez, N., Wu, Q., Wang, H., Griffiths, T. L., & Wang, M. (2024). Embodied LLM Agents Learn to Cooperate in Organized Teams (arXiv:2403.12482). arXiv.
Adenuga, I. J., Hanrahan, B. V., Wu, C., & Mitra, P. (2022). Living Documents: Designing for User Agency over Automated Text Summarization. In CHI Conference on Human Factors in Computing Systems Extended Abstracts (pp. 1-6).
Carroll, J. M. (2022). Why should humans trust AI?. Interactions, 29(4), 73-77.
Chen, J., Doryab, A., Hanrahan, B. V., Yousfi, A., Beck, J., Wang, X., ... & Carroll, J. M. (2019). Context-Aware Coproduction: Implications for Recommendation Algorithms. In International Conference on Information (pp. 565-577). Springer, Cham.
Cotter, K. (2022). Practical knowledge of algorithms: The case of BreadTube. New Media & Society. 14614448221081802
Cotter, K., DeCook, J. R., Kanthawala, S., & Foyle, K. (2022). In FYP We Trust: The Divine Force of Algorithmic Conspirituality. International Journal of Communication, 16, 1-23.
Huang, T. H., Lasecki, W., Azaria, A., & Bigham, J. (2016). " Is There Anything Else I Can Help You With?" Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 4, pp. 79-88).
Lee, S., Yu, R., Xie, J., Billah, S. M., & Carroll, J. M. (2022). Opportunities for human-AI collaboration in remote sighted assistance. In 27th International Conference on Intelligent User Interfaces (pp. 63-78).
Liao, Q. V., & Sundar, S. S. (2022). Designing for responsible trust in AI: A communication perspective. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22), 1257–1268.
Shen, H., & Huang, T. H. (2020). How useful are the machine-generated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 8, pp. 168-172).
Shin, D., Zhong, B., & Biocca, F. A. (2020). Beyond user experience: What constitutes algorithmic experiences? International Journal of Information Management, 52, 102061.
Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human-AI interaction (HAII). Journal of Computer-Mediated Communication, 25 (1), 74-88.
Sundar, S. S., & Lee, E.-J. (2022). Rethinking Communication in the Era of Artificial Intelligence. Human Communication Research, 48(3), 379–385.
You, Y., & Gui, X. (2020). Self-diagnosis through AI-enabled chatbot-based symptom checkers: user experiences and design considerations. In AMIA Annual Symposium Proceedings (Vol. 2020, p. 1354). American Medical Informatics Association.
Fabio, R. A., Plebe, A., & Suriano, R. (2024). AI-based chatbot interactions and critical thinking skills: An exploratory study. Current Psychology, 1-14.
Lim, S., Schmälzle, R., & Bente, G. (2024). Artificial Social Influence: Rapport-Building, LLM-Based Embodied Conversational Agents for Health Coaching. CONNECT Workshop: 24th ACM International Conference on Intelligent Virtual Agents.
Zhang, Y., & Liu, J. (2024). Falling behind again? Characterizing and assessing older adults' algorithm literacy in interactions with video recommendations. Journal of the Association for Information Science and Technology.
Chakrabarty, T., Laban, P., & Wu, C. S. (2024). Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits. arXiv preprint arXiv:2409.14509.
Kim, S. S., Liao, Q. V., Vorvoreanu, M., Ballard, S., & Vaughan, J. W. (2024, June). "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 822-835).
Sharma, N., Liao, Q. V., & Xiao, Z. (2024, May). Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-17).
Chancellor, S. (2023). Towards Practices for Human-Centered Machine Learning. Communications of the ACM, March 2023, Vol. 66 No. 3, Pages 78-85
Del Giudice, M., Scuotto, V., Orlando, B., & Mustilli, M. (2023). Toward the human – Centered approach. A revised model of individual acceptance of AI. Human Resource Management Review, 33(1), 100856.
Xu, W., Dainoff, M. J., Ge, L., & Gao, Z. (2023). Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI (arXiv:2105.05424). arXiv.
Kurfess, F., Vasilaky, K., Cheuk, T., Jenkins, R., Nolan, G., & Hajrasouliha, A. (2022). Assessment of Ethics and Social Justice Aspects in Data Science and Artificial Intelligence.
Lex, E., & Schedl, M. (2022). Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender Systems. ACM SIGIR Conference on Human Information Interaction and Retrieval, 367–368. https://doi.org/10.1145/3498366.3505841
Wang, A. Y., Wang, D., Drozdal, J., Muller, M., Park, S., Weisz, J. D., Liu, X., Wu, L., & Dugan, C. (2022). Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks. ACM Transactions on Computer-Human Interaction, 29(2), 1–33.
Umbrello, S., & van de Poel, I. (2021). Mapping value sensitive design onto AI for social good principles. AI and Ethics, 1(3), 283–296.
Zhou, M. X. (2021). Introduction to the Special Column for Human-Centered Artificial Intelligence. ACM Transactions on Interactive Intelligent Systems, 11(3–4), 1–1.
Inkpen, K. (2020). Does My AI Help or Hurt? Exploring Human-AI Complementarity. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (p. 2). Association for Computing Machinery.
Macdonald, N., Perks, M., & Reimann, R. (2004). Beyond human centered design? Proceedings of the 5th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, 373–374.
Margetis, G., Ntoa, S., Antona, M., & Stephanidis, C. (2021). Human-Centered Design of Artificial In℡ligence. In HANDBOOK OF HUMAN FACTORS AND ERGONOMICS (pp. 1085–1106). John Wiley & Sons, Ltd.
Wang, D., Maes, P., Ren, X., Shneiderman, B., Shi, Y., & Wang, Q. (2021). Designing AI to Work WITH or FOR People? Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 1–5.
Wang, Y., & Masoodian, M. (2020). Designing Visual Tools to Facilitate Human-Centered Design. Proceedings of the International Conference on Advanced Visual Interfaces, 1–3.
Nicenboim, I., Giaccardi, E., Søndergaard, M. L. J., Reddy, A. V., Strengers, Y., Pierce, J., & Redström, J. (2020). More-Than-Human Design and AI: In Conversation with Agents. Companion Publication of the 2020 ACM Designing Interactive Systems Conference, 397–400.
Peters, D., Vold, K., Robinson, D., & Calvo, R. A. (2020). Responsible AI—Two Frameworks for Ethical Design Practice. IEEE Transactions on Technology and Society, 1(1), 34–47.
Sankaran, S., Zhang, C., Gutierrez Lopez, M., & Väänänen, K. (2020). Respecting Human Autonomy through Human-Centered AI. Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, 1–3.
Schmidt, A. (2020). Interactive Human Centered Artificial Intelligence: A Definition and Research Challenges. Proceedings of the International Conference on Advanced Visual Interfaces, 1–4.
Stanford University, United States of America, & Auernhammer, J. (2020, September 10). Human-centered AI: The role of Human-centered Design Research in the development of AI. Design Research Society Conference 2020.
Sum, C. M., Alharbi, R., Spektor, F., Bennett, C. L., Harrington, C. N., Spiel, K., & Williams, R. M. (2022). Dreaming Disability Justice in HCI. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1–5.
Wang, D., Churchill, E., Maes, P., Fan, X., Shneiderman, B., Shi, Y., & Wang, Q. (2020). From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–6.
Shneiderman, B. (2020a). Human-Centered Artificial Intelligence: Three Fresh Ideas. AIS Transactions on Human-Computer Interactions, 12(3), 109–124.
Shneiderman, B. (2020b). Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems. ACM Transactions on Interactive Intelligent Systems, 10(4), 1–31.
Ramos, G., Suh, J., Ghorashi, S., Meek, C., Banks, R., Amershi, S., Fiebrink, R., Smith-Renner, A., & Bansal, G. (2019). Emerging Perspectives in Human-Centered Machine Learning. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 1–8.
Riedl, M. O. (2019). Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies, 1(1), 33–36.
Xu, W. (2019). Toward human-centered AI: A perspective from human-computer interaction. Interactions, 26(4), 42–46.
Benjamins, R., Barbado, A., & Sierra, D. (2019). Responsible AI by Design in Practice (arXiv:1909.12838). arXiv.
d’Aquin, M., Troullinou, P., O’Connor, N. E., Cullen, A., Faller, G., & Holden, L. (2018). Towards an “Ethics by Design” Methodology for AI Research Projects. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 54–59.
Zhu, J., Liapis, A., Risi, S., Bidarra, R., & Youngblood, G. M. (2018). Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation. 2018 IEEE Conference on Computational Intelligence and Games (CIG), 1–8.
Shneiderman, B. (2022). Human-Centered AI. Oxford University Press.
AI by Design—Google Books. (n.d.)
Addo, A., Centhala, S., & Shanmugam, M. (2020). Artificial Intelligence Design and Solution for Risk and Security. Business Expert Press.