Racial mirroring effects on human-agent interaction in psychotherapeutic conversations
By Yuting Liao, Jiangen He. 2020
53 citations recorded by the source corpus.
Conversational agents are increasingly utilized to deliver mental health interventions. However, these systems are characterized by relatively poor adoption and adherence. Our study explores the "racial mirroring" effects on how people perceive and engage with agents in the context of psychotherapy. We developed a conversational system with racially heterogeneous personas using strong visual cues. We conducted an experiment by randomly assigning participants (N=212) to racial mirroring, non-mirroring and control groups. Our results suggest that racial mirroring did influence human-agent interaction in terms of perceived interpersonal closeness, user satisfaction, disclosure comfort, desire to continue interacting, and projected future relationship with agents. In this paper, we present the conversational system, experimental procedure, and results. We conclude with design recommendations for employing conversational agents in mental health intervention.
Subfields: Applied Psychology, Artificial Intelligence, Social Psychology, Natural Language Processing
How researchers see this paper
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In Artificial Intelligence
- Iason Gabriel
- Po-Sen Huang
- Jonathan Uesato
- William S. Isaac
- John F. J. Mellor
- Borja Balle
- Geoffrey Irving
- Amelia Glaese
In Social Psychology
In Natural Language Processing
- Iason Gabriel
- John F. J. Mellor
- Borja Balle
- Geoffrey Irving
- Amelia Glaese
- Myra Cheng
- Lisa Anne Hendricks
- Po-Sen Huang
Citations in the corpus
- Ethical and social risks of harm from Language Models (2021, background): “The ethnicity of a conversational LM may be implied by its vocabulary, knowledge or vernacular (Marino, 2014), product description or name (e.g. ‘Jake - White’ vs ‘Darnell - Black’ vs ‘Antonio - Hispanic’ in (Liao and He, 2020)), or explicit self-description when prompted.”
- Taxonomy of Risks posed by Language Models (2022, background): “…identity markers may be implied by CA vocabulary, knowledge or vernacular [124]; product description, e.g. in one case where users could choose as virtual assistant Jake - White, Darnell - Black, Antonio - Hispanic [117]; or the CA’s explicit self-description during dialogue with the user.”
- The Whiteness of AI (2020, background): “Consequently, as Liao and He point out in their discussion of the racialisation of psychotherapeutic chatbots, “racial identity is an integral part of anthropomorphized agents” (Liao and He 2020, 2) .”