A Conceptual Framework for Machine Self-Presentation and Trust

Abstract

Increasingly, researchers are creating machines with humanlike social behaviors to elicit desired human responses such as trust and engagement, but a systematic characterization and categorization of such behaviors and their demonstrated effects is missing. This paper proposes a taxonomy of machine behavior based on what has been experimented with and documented in the literature to date. We argue that self-presentation theory, a psychosocial model of human interaction, provides a principled framework to structure existing knowledge in this domain and guide future research and development. We leverage a foundational human self-presentation taxonomy (Jones and Pittman, 1982), which associates human verbal behaviors with strategies, to guide the literature review of human-machine interaction studies we present in this paper. In our review, we identified 36 studies that have examined human-machine interactions with behaviors corresponding to strategies from the taxonomy. We analyzed frequently and infrequently used strategies to identify patterns and gaps, which led to the adaptation of Jones and Pittman’s human self-presentation taxonomy to a machine self-presentation taxonomy. The adapted taxonomy identifies strategies and behaviors machines can employ when presenting themselves to humans in order to elicit desired human responses and attitudes. Drawing from models of human trust we discuss how to apply the taxonomy to affect perceived machine trustworthiness.