My Ph.D. Dissertation, which summarizes this thread of work so far:
Qian Yang. "Profiling Artificial Intelligence as a Material for User Experience Design." PhD thesis. Carnegie Mellon University, 2020. >> pdf
Trying to articulate the design challenges AI distinctively poses to HCI:
[Best Paper Honorable Mention] Qian Yang, Aaron Steinfeld, Carolyn Rosé, and John Zimmerman. 2020. Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems - CHI ’20. >> pdf
Investigating the best practice of designing ML in industry:
Qian Yang, Alex Scuito, John Zimmerman, Jodi Forlizzi, and Aaron Steinfeld. 2018. Investigating How Experienced UX Designers Effectively Work with Machine Learning. In Proceedings of the 2018 ACM Conference on Designing Interactive Systems - DIS ’18. >> pdf
Mining ML technical advances in HCI literature and proposing a ML design framework:
Qian Yang, Nikola Banovic, and John Zimmerman. 2018. Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Research. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18. >> pdf
A reflection on why traditional user-centered design process doesn't fit ML and how to go about it:
Qian Yang. 2018. Machine Learning as a UX Design Material: How Can We Imagine Beyond Automation, Recommenders, and Reminders?. To appear in The AAAI 2018 Spring Symposium on Designing the User Experience of Artificial Intelligence Technical Report. >> AAAI copy
A position paper on designing the right ML thing and designing it right:
Qian Yang. 2017. The Role of Design in Creating Machine-Learning-Enhanced User Experience. In The AAAI 2017 Spring Symposium on Designing the User Experience of Machine Learning Systems Technical Report SS-17-04, 406–411.  >> pdf
Other wonderful papers of AAAI'17S Sympodium on UX of Machine Learning are available here.
Thank you!
Back to Top