From teaching AI to research
I got my start in the AI field in 2019 working as a computer science educator/curriculum designer. I taught middle school students how to code self-driving robots in a visual programming language like Scratch.
Demo'ing how to code an AI robot
In that role, I saw first hand the impact AI was having on the next generation. It was overdue for a deep look at how to make AI work for humans, not the other way around.
Giving a talk on teaching AI to K-12 at the AAAI conference
When I got to CMU for my masters, I focused my research on human-AI collaboration: partnerships between humans and AI systems that build upon each other's strength.
What I learned
No time to read through 100 pages of academic research? I got you. Here is what I learned and how it applies to industry.
Are you over-relying on AI?
The world's gnarliest problems are solved by AI and humans together leveraging each other's strengths to achieve best outcomes. However, it is very hard to assess the skills of both human and AI and very easy to overestimate AI capabilities. I believe one of the ways to solve it is to train workers, give them adequate time to adept, and regularly assess skills.
Publications
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
🏆 Best Paper Honorable Mention
Anna Kawakami, Venkatesh Sivaraman, Hao-Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, and Kenneth Holstein. In ACM Conference on Human Factors in Computing Systems, CHI 2022.
How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions
Hao-Fei Cheng, Logan Stapleton*, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, and Haiyi Zhu. In ACM Conference on Human Factors in Computing Systems, CHI 2022.
Towards a Learner-Centered Explainable AI
Anna Kawakami, Luke Guerdan, Yanghuidi Cheng, Anita Sun, Alison Hu, Kate Glazko, Nikos Arechiga, Matthew Lee, Scott Carter, Haiyi Zhu and Kenneth Holstein. Workshop on Human-Centered Explainable AI (HCXAI) at the ACM Conference on Human Factors in Computing Systems, CHI 2022.
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables
Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumai, Yanghuidi Cheng. Submitted to CSCW 2023.
See more on my Google Scholar
