Six reasons to study emotional support from conversational artificial intelligence
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Karnaze and Bloss (2026) note that there are presently no FDA-approved LLMs and no established best practices for enterprise-level deployment in healthcare or advisory contexts, a regulatory regime gap with direct consequences for user autonomy.
“Karnaze and Bloss (2026) note that there are presently no FDAapproved LLMs and no established best practices for enterprise-level deployment in healthcare or advisory contexts, a regulatory regime gap with direct consequences for user autonomy that existing frameworks have not yet closed. 25”
From The hidden functions of sycophancy in AI systems: steering, consistency, and cognitive dependency (2026) · cites this paper as Karnaze, M. and Bloss, C. (2026)
Recent empirical work, including Sharma and Tong (2026), Randazzo et al. (2026), and Karnaze and Bloss (2026), documents the range and scale of downstream effects with increasing precision.
“Recent empirical work, including Sharma and Tong. (2026), Randazzo etal. (2026), and Karnaze and Bloss (2026), documents the range and scale of these downstream effects with increasing precision.”
From The hidden functions of sycophancy in AI systems: steering, consistency, and cognitive dependency (2026) · cites this paper as Karnaze and Bloss, 2026
Cites
Hutchins, E. (1995) · doi:10.7551/mitpress/1881.001.0001 · Cited by 10,801 · see citing works
The negative side of social interaction: Impact on psychological well-being.
Rook, K. S. (1984) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.46.5.1097 · Cited by 1,645 · see citing works
Loftus, E. F. (2005) · Learning & Memory · doi:10.1101/lm.94705 · Cited by 1,297 · see citing works
Sociodemographic biases in medical decision making by large language models
Omar, M., Soffer, S., Agbareia, R., Bragazzi, N. L., Apakama, D. U., Horowitz, C. R., Charney, A. W., Freeman, R., Kummer, B., Glicksberg, B. S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03626-6 · Cited by 173 · see citing works
Iftikhar, Z., Xiao, A. W., RANSOM, S. D., et al. (2025) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1609/aies.v8i2.36632 · Cited by 59 · see citing works
Habicht, J., Dina, L.-M., McFadyen, J., Stylianou, M., Harper, R., Hauser, T. U., & Rollwage, M. (2025) · Journal of Medical Internet Research · doi:10.2196/60435 · Cited by 52 · see citing works
AI systems must not confuse users about their sentience or moral status
Schwitzgebel, E. (2023) · Patterns · doi:10.1016/j.patter.2023.100818 · Cited by 49 · see citing works
Artificial intimacy: ethical issues of AI romance
Shank, D. B., Koike, M., & Loughnan, S. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2025.02.007 · Cited by 19 · see citing works
Social Ecological Systems in Flux
Lejano, R. P., & Stokols, D. (2024) · Annual Review of Sociology · doi:10.1146/annurev-soc-083023-034136 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The hidden functions of sycophancy in AI systems: steering, consistency, and cognitive dependency
Jacobowitz, S. (2026) · AI & SOCIETY · doi:10.1007/s00146-026-02993-z · Cited by 4 · see citing works
“(2026), Randazzo et al. (2026), and Karnaze and Bloss (2026), documents the range and scale of these downstream effects with increasing precision.”
- Authors
- Karnaze, M. M., & Bloss, C. S.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Karnaze, Melissa M.
- Venue
- Nature Human Behaviour
- Published
- 2026-02-13
- Volume
- 10
- Issue
- 4
- Pages
- 635-638
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W7128813478
- Cited by
- 4
- References
- 9
- Citations by year
- 2026: 4
- FWCI
- 33.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Center for Empathy and Technology, withing the Sanford Institute for Empathy and Compassion at UC San Diego
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- conversational AI, emotional support, human-AI interaction, chatbots, research agenda, AI policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
