Why effective and Ethical AI needs human-centered design

Abstract: 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, expressing empathy, or ethical reasoning.

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. This session will discuss principles of human-computer collaboration, organize them into a framework, and offer 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, the 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. A number of AI examples will be used to illustrate these principles.

Bio: Jim is the US chief data scientist of Deloitte Consulting, and a member of Deloitte’s Advanced Analytics and Modeling practice. Jim has extensive experience applying predictive analytics techniques in a variety of public and private sector domains. He has also spearheaded Deloitte’s use of behavioral nudge tactics to more effectively act on model indications and prompt behavior change. Jim is a former professor at the University of Wisconsin-Madison business school, and he holds a PhD in the Philosophy of Science from The University of Chicago. Jim is a Fellow of the Casualty Actuarial Society and on its board of directors.