Pitfalls of Embodiment in Human-Agent Experiment Design
The intelligent virtual agent community often works from the assumption that embodiment confers clear benefits to human-machine interaction. However, embodiment has potential drawbacks in highlighting the salience of social stereotypes such as those around race and gender. Indeed, theories of computer-mediated communication highlight that visual anonymity can sometimes enhance team outcomes. Negotiation is one domain where social perceptions can impact outcomes. For example, research suggests women perform worse in negotiations and find them more aversive, particularly when interacting with men opponents. Research with human participants makes it challenging to unpack whether these negative consequences stem from women’s perceptions of their partner or greater toughness on the part of these men opponents. We use a socially intelligent AI negotiation agent to begin to unpack these processes. We manipulate the perceived toughness of the AI by whether or not it expresses anger — a common tactic to extract concessions. Independently, we manipulate the activation of stereotypes by randomly setting whether the interaction has embodiment (as a male opponent) or has only text (where we obscure gender cues). We find a clear interaction between gender and embodiment. Specifically, women perform worse, and men perform better against an apparently male opponent compared to a disembodied agent – as measured by the subjective value they assign to their outcome. This highlights the potential disadvantages of embodiment in negotiation, though future research must rule out alternative mechanisms that might explain these results.
Authors
- James Hale (ORCID: https://orcid.org/0000-0003-0596-3909)
- Jonathan Gratch (ORCID: https://orcid.org/0000-0002-5959-809X)
- Lindsey Schweitzer
Institutions
- University of Southern California (US)
- Middlebury College (US)
Publication Details
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1145/3652988.3673958
- Citations
- 2
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 1.05