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April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

Executive Briefing: Why AI needs human-centered design

James Guszcza (Deloitte Consulting)
4:00pm–4:40pm Tuesday, May 1, 2018
Average rating: ****.
(4.80, 5 ratings)

What you'll learn

  • Discover the principles of human psychology that are essential to designing effective AI applications


Modern AI is less about creating human-like general intelligence than it is about creating tools that do cognitive spade work and more generally enhance or extend human intelligence. AI tools based on statistical learning, big data, and pattern recognition can perform a growing number of tasks that are difficult or impossible for humans to perform. However, they perform poorly at many aspects of cognition that come naturally to humans: formulating hypotheses, understanding cause and effect relationships, using commonsense reasoning, picking up on social cues and nonverbal forms of communication, and expressing empathy.

The complementary nature of human and algorithmic intelligence points to the need for an interdisciplinary approach that draws on such fields as computer science, human psychology, behavioral economics, and design thinking: designing collaboration systems that enable forms of human-computer collective intelligence. James Guszcza discusses principles of human-computer collaboration, organizes them into a framework, and offers several real-life examples in which human-centered design has been crucial to the economic success of an AI project. Concepts covered will relate to both System 2 cognition (“thinking slow”) and System 1 cognition (“thinking fast”). Regarding the former, JCR Licklider’s notion of human-computer symbiosis is relevant: algorithms are good at what humans are poor at and vice versa. Regarding the latter, behavioral economics teaches us that prompting smarter choices and decisions often involves more than providing information or setting up incentives. Often the way information is presented or choices are arranged has surprisingly large effects on end-user behavior. Thus AI systems will often benefit from insightful uses of choice architecture. James shares a number of AI examples to illustrate these principles.

Photo of James Guszcza

James Guszcza

Deloitte Consulting

James Guszcza is chief data scientist at Deloitte and a pioneering member of Deloitte’s original data science practice, where he has applied statistical and machine learning methods to such diverse business problems as healthcare utilization, customer and employee retention, talent management, customer segmentation, insurance pricing and underwriting, credit scoring, child support enforcement, patient safety, claims management, and fraud detection. He also spearheaded Deloitte’s use of behavioral nudge tactics to more effectively act on model indications. A frequent author and conference speaker, Jim designs and teaches hands-on business analytics training seminars for both the Society of Actuaries and the Casualty Actuarial Society, of which he is a fellow and a member of its board of directors. Jim is a former professor at the University of Wisconsin-Madison business school. He holds a PhD in the philosophy of science from the University of Chicago.