Ken Holstein - Designing for complementarity in AI-augmented work
Host
Arvind Satyanarayan
CSAIL MIT
Abstract:
AI systems are increasingly used to augment human work in complex social and creative contexts, including social work, education, design, and content moderation. These technologies are typically introduced with the promise of overcoming human limitations and biases. However, AI judgments in such settings are themselves likely to be imperfect and biased, even if in different ways than humans. In this talk, I argue that today’s worker-facing AI systems often represent missed opportunities to meaningfully complement and enhance human workers’ abilities. I will present real-world case studies and conceptual frameworks from our research, which illustrate critical challenges to achieving human-AI complementarity in practice. I will then share some of our efforts to overcome these challenges, targeting various points across the AI project lifecycle—from the earliest problem formulation stages, to the design of AI system evaluations, to the development of worker-AI interfaces.
Bio:
Ken Holstein is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University, where he leads the Co-Augmentation, Learning, & AI (CoALA) Lab:
https://www.thecoalalab.com/. Their research focuses on supporting effective forms of AI-augmented work in real-world contexts, and on scaffolding more responsible AI development practices in industry and the public sector. Throughout their work, they draw on approaches from human-computer interaction, AI, design, psychology, statistics, and the learning sciences. Their work has received awards at ACM CHI, IEEE SaTML, AIED, and ICLS, and has been covered by outlets such as PBS, Wired, Forbes, The Boston Globe, The Hechinger Report, and Marketplace Tech.
The talk will also be streaming live over Zoom: https://mit.zoom.us/j/91645609513.
AI systems are increasingly used to augment human work in complex social and creative contexts, including social work, education, design, and content moderation. These technologies are typically introduced with the promise of overcoming human limitations and biases. However, AI judgments in such settings are themselves likely to be imperfect and biased, even if in different ways than humans. In this talk, I argue that today’s worker-facing AI systems often represent missed opportunities to meaningfully complement and enhance human workers’ abilities. I will present real-world case studies and conceptual frameworks from our research, which illustrate critical challenges to achieving human-AI complementarity in practice. I will then share some of our efforts to overcome these challenges, targeting various points across the AI project lifecycle—from the earliest problem formulation stages, to the design of AI system evaluations, to the development of worker-AI interfaces.
Bio:
Ken Holstein is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University, where he leads the Co-Augmentation, Learning, & AI (CoALA) Lab:
https://www.thecoalalab.com/. Their research focuses on supporting effective forms of AI-augmented work in real-world contexts, and on scaffolding more responsible AI development practices in industry and the public sector. Throughout their work, they draw on approaches from human-computer interaction, AI, design, psychology, statistics, and the learning sciences. Their work has received awards at ACM CHI, IEEE SaTML, AIED, and ICLS, and has been covered by outlets such as PBS, Wired, Forbes, The Boston Globe, The Hechinger Report, and Marketplace Tech.
The talk will also be streaming live over Zoom: https://mit.zoom.us/j/91645609513.