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Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support

Nature Machine Intelligence volume 5, pages 46–57 (2023)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks such as scheduling meetings and grammar-checking text. However, such human–AI collaboration poses challenges for more complex tasks, such as carrying out empathic conversations, due to the difficulties that AI systems face in navigating complex human emotions and the open-ended nature of these tasks. Here we focus on peer-to-peer mental health support, a setting in which empathy is critical for success, and examine how AI can collaborate with humans to facilitate peer empathy during textual, online supportive conversations. We develop HAILEY, an AI-in-the-loop agent that provides just-in-time feedback to help participants who provide support (peer supporters) respond more empathically to those seeking help (support seekers). We evaluate HAILEY in a non-clinical randomized controlled trial with real-world peer supporters on TalkLife (N = 300), a large online peer-to-peer support platform. We show that our human–AI collaboration approach leads to a 19.6% increase in conversational empathy between peers overall. Furthermore, we find a larger, 38.9% increase in empathy within the subsample of peer supporters who self-identify as experiencing difficulty providing support. We systematically analyse the human–AI collaboration patterns and find that peer supporters are able to use the AI feedback both directly and indirectly without becoming overly reliant on AI while reporting improved self-efficacy post-feedback. Our findings demonstrate the potential of feedback-driven, AI-in-the-loop writing systems to empower humans in open-ended, social and high-stakes tasks such as empathic conversations.

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Fig. 1: A randomized controlled trial with 300 TalkLife peer supporters as participants.
Fig. 2: Randomized controlled trial demonstrates that Human–AI collaboration enables more empathic conversations.
Fig. 3: The derived hierarchical taxonomy of human–AI collaboration categories.

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Data availability

Data used for training the empathy classification model used for automatic evaluation are available at https://github.com/behavioral-data/Empathy-Mental-Health29,83. Data used for training PARTNER and the data collected in our randomized controlled trial are available on request from the corresponding author with a clear justification and a license agreement from TalkLife.

Code availability

Source code of the empathy classification model used for automatic evaluation is available at https://github.com/behavioral-data/Empathy-Mental-Health29,83. Source code of PARTNER is available at https://github.com/behavioral-data/PARTNER47,56. Code used for designing the interface of HAILEY is available at https://github.com/behavioral-data/Human-AI-Collaboration-Empathy84. Code used for the analysis of the study data is available on request from the corresponding author. For the most recent project outcomes and resources, please visit https://bdata.uw.edu/empathy.

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Acknowledgements

We thank TalkLife and J. Druitt for supporting this work, for advertising the study on their platform and for providing us access to a TalkLife data set. We also thank members of the UW Behavioral Data Science Group, Microsoft AI for Accessibility team and D.S. Weld for their suggestions and feedback. T.A., A.S. and I.W.L. were supported in part by NSF grant IIS-1901386, NSF CAREER IIS-2142794, NSF grant CNS-2025022, NIH grant R01MH125179, Bill & Melinda Gates Foundation (INV-004841), the Office of Naval Research (#N00014-21-1-2154), a Microsoft AI for Accessibility grant and a Garvey Institute Innovation grant. A.S.M. was supported by grants from the National Institutes of Health, National Center for Advancing Translational Science, Clinical and Translational Science Award (KL2TR001083 and UL1TR001085) and the Stanford Human-Centered AI Institute. D.C.A. was supported by NIH career development award K02 AA023814.

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Authors and Affiliations

  1. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Ashish Sharma, Inna W. Lin & Tim Althoff

  2. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Adam S. Miner

  3. Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA

    Adam S. Miner

  4. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA

    David C. Atkins

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Contributions

A.S., I.W.L., A.S.M., D.C.A. and T.A. were involved with the design of HAILEY and the formulation of the study. A.S. and I.W.L. conducted the study. All authors interpreted the data, drafted the manuscript and made significant intellectual contributions to the manuscript.

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Correspondence to Tim Althoff.

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Competing interests

D.C.A. is a co-founder with equity stake in a technology company, Lyssn.io, focused on tools to support training, supervision and quality assurance of psychotherapy and counselling. The remaining authors declare no competing interests.

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Sharma, A., Lin, I.W., Miner, A.S. et al. Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nat Mach Intell 5, 46–57 (2023). https://doi.org/10.1038/s42256-022-00593-2

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