Toward a Framework for Machine Self-Presentation : A survey of self-presentation strategies in human-machine interaction studies

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. Of those studies utilizing self-presentation behaviors for machines, the majority have employed a strategy of Ingratiation, while relatively few have employed strategies of Supplication, Self-promotion, Exemplification, and Intimidation. The primary contribution of this research is our analysis of the 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. Approved for Public Release; Distribution Unlimited. Public Release Case Number 19-3566.