Indiana University Indiana University IU

Event: Dr. Kinga Makovi, NYU Abu Dhabi


Norms of fairness influence trust in human-machine interaction



Monday, Apr 26th 2021 at 11:00 AM
Zoom (registration required)


Registration is required: https://iu.zoom.us/meeting/register/tZckdO2hpj0uH9FdeFlLFFaCxAbGSkx_C9JQ 

Dr. Makovi has some availability to meet with faculty and students, contact us as iuni@iu.edu if you would like to be included. 

 

Norms of fairness influence trust in human-machine interaction

Abstract: People increasingly interact with machines as a hybrid human-bot society is bound to emerge. Ample work has documented that social order is facilitated by norms that foster prosocial behavior and trust in anonymous strangers. However, it is unclear whether similar foundations exist for hybrid human-bot societies, or if further adaptation is needed to maintain high levels of prosociality in such settings. To address this fundamental issue, we design a series of experiments where we manipulate the identity of interaction partners by introducing bots to study the levels of trust in people and bots. We find that: (1) when bots are fair or punish the unfair, they inspire trust, albeit to a lesser extent compared to people; (2) when people are fair to bots or punish those who are unfair to bots they signal trustworthiness, but to a lesser extent compared to when people are the beneficiaries; (3) people’s belief about the consensus held regarding norms of fairness and third-party punishment influences the trust that can be gained from these behaviors, regardless of whether the interaction partners are bots or people; finally (4) manipulating peoples’ belief about norm-consensus can be an effective tool to increase the trust-gain of bots who are fair. Our findings not only offer practical guidance for enhancing human-machine interaction but suggest that social order is likely to emerge in human-bot societies.

 

Bio: Kinga Makovi is an assistant professor at NYU Abu Dhabi in Social Research and Public Policy. She holds an MS in mathematical economics from Corvinus University of Budapest and a PhD in sociology from Columbia University. Her dissertation was supported by an NSF Dissertation Improvement Grant and she received the de Karman Fellowship. Her work appeared in Social Forces, Sociological Science and Scientific Reports. She examines the determinants of behavioral convergence including diffusion on networks and norm convergence using computational methods and experimental approaches.