ABSTRACT:

Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks, while taking human needs into consideration and preserving human control. In this short position paper, we use a Data Science (DS) as an example application to illustrate how we approach HCAI research. The AI techniques built for supporting DS works are collectively referred to as AutoML systems, and their goals are to automate some human works of the DS workflow. We illustrate a three-step systematical research approach (i.e., explore, build, and integrate) and four practical ways of implementation for HCAI systems. We argue that our work is a cornerstone towards the ultimate future of Human-AI Collaboration for DS and beyond, where AI and humans can take complementary and indispensable roles to achieve a better outcome and experience.

BIO:

Dr. Dakuo Wang is a Research Staff Member at IBM Research AI, and Principal Investigator at MIT-IBM Watson AI Lab, based at Cambridge, MA. His research lies in the intersection between human-computer interaction (HCI) and artificial intelligence (AI). Before joining IBM Research, Dakuo got his Ph.D. from the University of California Irvine (advisor: Judith Olson and Gary Olson). He is now leading a team to conduct research and design user experience for IBM AutoAI. Outside his IBM work, Dakuo is also broadly interested in how human interact with real-world AI applications such as conversational agents and clinical decision support systems (Human-AI Collaboration). ACM has recognized Dakuo Wang as an ACM Distinguished Speaker.

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