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Cited by 14Digital Mental Health InterventionsEthics and Social Impacts of AISocial Robot Interaction and HRI

Does a lack of emotions make chatbots unfit to be psychotherapists?

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Rahsepar Meadi, Mehrdad, Bernstein, Justin S., Batelaan, Neeltje, van Balkom, Anton J. L. M., Metselaar, Suzanne·2024·Bioethics
DOI 10.1111/bioe.13299
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Abstract

Mental health chatbots (MHCBs) designed to support individuals in coping with mental health issues are rapidly advancing. Currently, these MHCBs are predominantly used in commercial rather than clinical contexts, but this might change soon. The question is whether this use is ethically desirable. This paper addresses a critical yet understudied concern: assuming that MHCBs cannot have genuine emotions, how this assumption may affect psychotherapy, and consequently the quality of treatment outcomes. We argue that if MHCBs lack emotions, they cannot have genuine (affective) empathy or utilise countertransference. Consequently, this gives reason to worry that MHCBs are (a) more liable to harm and (b) less likely to benefit patients than human therapists. We discuss some responses to this worry and conclude that further empirical research is necessary to determine whether these worries are valid. We conclude that, even if these worries are valid, it does not mean that we should never use MHCBs. By discussing the broader ethical debate on the clinical use of chatbots, we point towards how further research can help us establish ethical boundaries for how we should use mental health chatbots.

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  • A lack of empathy in LLM-CBs may render certain psychotherapeutic features such as countertransference ineffective.

    “A lack of empathy may also render certain psychotherapeutic features such as countertransference ineffective52.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [52]

    Section 5 says that if mental health chatbots lack emotions, they cannot experience countertransference and patients may lose its potential benefits. It also distinguishes countertransference from empathy; the paper attributes the loss of countertransference to a lack of emotions, not specifically to a lack of empathy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Authors
Rahsepar Meadi, M., Bernstein, J. S., Batelaan, N., van Balkom, A. J. L. M., & Metselaar, S.
Affiliations
  • Department of Psychiatry, Amsterdam Public Health, Mental Health program Amsterdam UMC location Vrije Universiteit Amsterdam Amsterdam The Netherlands
  • Vrije Universiteit Amsterdam (NL)
  • Amsterdam Public Health (NL)
  • Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
ORCID
Venue
Bioethics
Publisher
Wiley
Published
2024-05-12
Volume
38
Issue
6
Pages
503-510
ISSN
  • 0269-9702 (ISSN-L)
  • 1467-8519
DOI
10.1111/bioe.13299
OpenAlex
W4396845061
Cited by
14
References
0
Citations by year
2026: 4 · 2025: 8 · 2024: 2
FWCI
5.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioethics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Pure Amsterdam UMC (publishedVersion)
  • VU Research Portal (submittedVersion, other-oa)
Funders
Technische Universiteit Delft
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords
mental health chatbots, affective empathy, countertransference, chatbot ethics, patient harm, treatment outcomes
MeSH terms
Psychotherapists, Adaptation, Psychological, Mental Disorders, Countertransference, Emotions, Empathy, Humans, Mental Health, Psychotherapy

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