Karen Huang - Faculty & Research - Harvard Business School
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Karen Huang

Doctoral Student

I am a PhD candidate in Organizational Behavior (Psychology Track), a joint program between the Harvard Business School and the Harvard Graduate School of Arts & Sciences, and a Master’s candidate in the Department of Psychology. I earned my B.A. in Ethics, Politics & Economics from Yale University, during which I also studied moral philosophy at the University of Cambridge and phenomenology at Bard College Berlin.

I am broadly interested in the psychology and ethics of emerging technologies.

In my dissertation work, combining methods from experimental psychology and natural language processing, I study how question-asking increases responsiveness and liking in dyadic conversation (published in Journal of Personality and Social Psychology). Currently I am researching how AI could assist human-to-human dialogue, and the features that make human-to-human dialogue uniquely human. I am also interested in applications to chatbot development.

In a second stream of research, I investigate fairness judgments, particularly regarding AI and automated systems. In current work in progress, I study the effect of impartial reasoning on utilitarian judgments and policy attitudes toward autonomous vehicles. In another ongoing project, I research fairness judgments in bail, loan, and affirmative action decisions in order to help develop fair decision-making algorithms.

I've taught as a Teaching Fellow for the psychology course Evolving Morality, which applies moral psychology and philosophy to topics in automation and AI. In addition, I have written negotiation simulations and assisted with course development and teaching in the Negotiation course in the MBA curriculum.

My research has been covered in media outlets such as National Public Radio (Morning Edition), Freakonomics Radio, Bloomberg, and Forbes.

Journal Articles
  1. It Doesn't Hurt to Ask: Question-asking Increases Liking

    K. Huang, M. Yeomans, A.W. Brooks, J. Minson and F. Gino

    Conversation is a fundamental human experience, one that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational behavior: question-asking. Across three studies of live dyadic conversations, we identify a robust and consistent relationship between question-asking and liking: people who ask more questions are better liked by their conversation partners. When people are instructed to ask more questions, they are perceived as higher in responsiveness, an interpersonal construct that captures listening, understanding, validation, and care. We measure responsiveness with an attitudinal measure from previous research as well as a novel behavioral measure: the number of follow-up questions one asks. In both cases, responsiveness explains the effect of question-asking on liking. In addition to analyzing live get-to-know-you conversations online, we also studied face-to-face speed-dating conversations. We find that speed daters who ask more questions during their dates are more likely to elicit agreement for second dates from their partners, a behavioral indicator of liking. We trained a natural language processing algorithm as a “follow-up question detector” that we applied to our speed-dating data (and can be applied to any text data to more deeply understand question-asking dynamics). The follow-up question rate established by the algorithm explained why question-asking led to speed-dating success. We also find that, despite the persistent and beneficial effects of asking questions, people do not anticipate that question-asking increases interpersonal liking.

    Keywords: question-asking; liking; responsiveness; Conversation; natural language processing; Interpersonal Communication; Behavior;


    Huang, K., M. Yeomans, A.W. Brooks, J. Minson, and F. Gino. "It Doesn't Hurt to Ask: Question-asking Increases Liking." Journal of Personality and Social Psychology 113, no. 3 (September 2017): 430–452.  View Details
Working Papers