Juliana Schroeder Publishes “Trusting in Machines – How Mode of Interaction Affects Willingness to Share Personal Information with Machines”

06 Jan 2018

CHAI affiliate Juliana Schroeder along with Matthew Schroeder presented this paper at the Hawaii International Conference on System Sciences, Big Island, HI. We would like to congratulate her on the paper being nominated for the best paper award! The abstract reads:

Every day, people make decisions about whether to trust machines with their personal information, such as letting a phone track one’s location. How do people decide whether to trust a machine? In a field experiment, we tested how two modes of interaction— expression modality, whether the person is talking or typing to a machine, and response modality, whether the machine is talking or typing back—influence the willingness to trust a machine. Based on research that expressing oneself verbally reduces self-control compared to nonverbal expression, we predicted that talking to a machine might make people more willing to share their personal information. Based on research on the link between anthropomorphism and trust, we further predicted that machines who talked (versus texted) would seem more human-like and be trusted more. Using a popular chatterbot phone application, we randomly assigned over 300 community members to either talk or type to the phone, which either talked or typed in return. We then measured how much participants anthropomorphized the machine and their willingness to share their personal information (e.g., their location, credit card information) with it. Results revealed that talking made people more willing to share their personal information than texting, and this was robust to participants’ self-reported comfort with technology, age, gender, and conversation characteristics. But listening to the application’s voice did not affect anthropomorphism or trust compared to reading its text. We conclude by considering the theoretical and practical implications of this experiment for understanding how people trust machines.