Humans and AI are more closely interacting than ever before, in all areas of our work, education, and life. As more intelligent machines are entering our lives, their accuracy and performance are not the only important factor that matters. As designers of such technology, we have to carefully consider the user experience of AI in order for AI to be of practical value. In this course, we will explore various dimensions of human-AI interaction, including ethics, explainability, design process involving AI, visualization, human-AI collaboration, recommender systems, and a few notable application areas.
A side goal of this course is to encourage all of us to bridge the gap between the two fields of HCI and AI. As a step toward this vision, we want to encourage students with HCI and AI background to mingle, interact, discuss, and collaborate through this course. We expect most students taking this course to have background knowledge in either HCI or AI through at least intro-level coursework. If you’re unsure if you meet this criterion, please contact the course staff immediately. Having background in both is great, although not required.
This is a highly interactive class: You’ll be expected to actively participate in activities, projects, assignments, and discussions. There will be no lectures or exams. Major course activities include:
| Week | Date | Instructor | Topic | Reading (response indicates a reading response is required for the material.) | Due |
|---|---|---|---|---|---|
| 1 | 3/2 | Kim | Introduction & Course Overview | ||
| 1 | 3/4 | Kim | A Quick Tour of Human-AI Interaction |
(1) Licklider, Joseph CR. "Man-computer symbiosis." IRE transactions on human factors in electronics 1 (1960): 4-11.
(2) Shyam Sankar. The Rise of Human Computer Cooperation. TED Talk Video, 2012 (12 mins). |
|
| 2 | 3/9 | Song | Primer on AI (Part 1) |
(1) response Lubars, Brian, and Chenhao Tan. "Ask not what AI can do, but what AI should do: Towards a framework of task delegability." In Advances in Neural Information Processing Systems, pp. 57-67. 2019.
(2) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). pp. 6000–6010. 2017. |
RR by all |
| 2 | 3/11 | Song | Primer on AI (Part 2) | Tutorial |
(1) response Xu, Anbang, Zhe Liu, Yufan Guo, Vibha Sinha, and Rama Akkiraju. "A new chatbot for customer service on social media." In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 3506-3510. 2017.
(2) Nityesh Agarwal. "Getting started with reading Deep Learning Research papers: The Why and the How", a blog post at Towards Data Science (2018). |
RR by A |
| 3 | 3/16 | Kim | Primer on HCI (Part 1) | Tutorial |
(1) response Amershi, Saleema, et al. "Guidelines for human-AI interaction." Proceedings of the 2019 chi conference on human factors in computing systems. 2019.
(2) Google PAIR. People + AI Guidebook. Published May 8, 2019. |
RR by B Assignment #1 announced |
| 3 | 3/18 | Kim | Primer on HCI (Part 2) | Tutorial |
(1) response Shneiderman, B., "Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy." International Journal of Human-Computer Interaction 36, 6, 495-504. 2020.
(2) Henriette Cramer and Juho Kim. "Confronting the tensions where UX meets AI." interactions 26.6 (2019): 69-71. |
RR by A |
| 4 | 3/23 | Kim | Ethics and FAccT of AI (Part 1) |
(1) response Davidson, Thomas, Debasmita Bhattacharya, and Ingmar Weber. "Racial bias in hate speech and abusive language detection datasets." arXiv preprint arXiv:1905.12516 (2019).
(2) Bolukbasi, Tolga, et al. "Man is to computer programmer as woman is to homemaker? debiasing word embeddings." Advances in Neural Information Processing Systems. 2016. |
RR by B |
| 4 | 3/25 | Kim | Ethics and FAccT of AI (Part 2) |
(1) response Timnit Gebru. "Computer vision in practice: who is benefiting and who is being harmed?" (video, 51 mins) Slides
(2) Kate Crawford and Trevor Paglen, “Excavating AI: The Politics of Training Sets for Machine Learning" (September 19, 2019) |
RR by A |
| 5 | 3/30 | Song | Historical Perspectives on Human-AI Interaction |
(1) response Horvitz, Eric. "Principles of mixed-initiative user interfaces." In Proceedings of the SIGCHI conference on Human Factors in Computing Systems, pp. 159-166. 1999.
(2) Ben Schneiderman and Pattie Maes. "Direct Manipulation vs. Interface Agents". Interactions 1997. |
RR by B Assignment #1 DUE |
| 5 | 4/1 | Song | Metrics to Measure Human-AI Performance |
(1) response Gagan Bansal, Besmira Nushi, Ece Kamar, et al. "Beyond accuracy: The role of mental models in human-AI team performance." In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 2019.
(2) Matthew Kay, Shwetak N. Patel, and Julie A. Kientz. "How good is 85%? A survey tool to connect classifier evaluation to acceptability of accuracy." In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015. |
RR by A |
| 6 | 4/6 | Song | Interpretable and Explainable AI (Part 1) |
(1) response Marco Tulio Ribeiro, Sameer Singh, and Carlos 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. (2) Zachary C. Lipton. "The mythos of model interpretability." 2018. |
RR by B |
| 6 | 4/8 | Song | Interpretable and Explainable AI (Part 2) |
(1) response Daniel S. Weld, and Gagan Bansal. "The challenge of crafting intelligible intelligence." Communications of the ACM. 2019. (2) Alison Smith-Renner, Ron Fan, Melissa Birchfield, et al. "No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML." In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020. |
RR by A |
| 7 | 4/13 | Kim | AI and Crowds |
(1) response Jennifer Wortman Vaughan. 2018. Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research. Journal of Machine Learning Research 18, 193: 1–46. *** Instructor note: the sections 3 and 5 could be skimmed. (2) Aniket Kittur, Jeffrey V. Nickerson, Michael Bernstein, et al. The Future of Crowd Work. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW '13), 1301–1318. 2018. |
RR by B |
| 7 | 4/15 | Both | Project proposal feedback | Assignment #2 announced | |
| 8 | 4/20 | No class (Midterms week) | |||
| 8 | 4/22 | No class (Midterms week) | |||
| 9 | 4/27 | Kim | AI Design Process |
(1) response Mitchell, Margaret, et al. "Model cards for model reporting." Proceedings of the conference on fairness, accountability, and transparency. 2019. (2) Sculley, David, et al. "Hidden technical debt in machine learning systems." Advances in neural information processing systems. 2015. |
RR by A |
| 9 | 4/29 | Song | InfoViz and Data Visualization |
(1) response Kay, Matthew, Tara Kola, Jessica R. Hullman, and Sean A. Munson. When (ish) is my bus? user-centered visualizations of uncertainty in everyday, mobile predictive systems. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2016. (2) Amershi, Saleema, Max Chickering, Steven M. Drucker, Bongshin Lee, Patrice Simard, and Jina Suh. Modeltracker: Redesigning performance analysis tools for machine learning. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015. |
RR by B |
| 10 | 5/4 | ||||
| 10 | 5/6 | Assignment #2 DUE | |||
| 11 | 5/11 | ||||
| 11 | 5/13 | ||||
| 12 | 5/18 | ||||
| 12 | 5/20 | ||||
| 13 | 5/25 | ||||
| 13 | 5/27 | ||||
| 14 | 6/1 | ||||
| 14 | 6/3 | ||||
| 15 | 6/8 | ||||
| 15 | 6/10 | ||||
| 16 | 6/15 | ||||
| 16 | 6/17 |