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AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read People with lived experience of depression value five things in mental health chatbots, and harms map to those values.

desk verdict A competent, honest values-harms mapping for mental-health chatbots with one genuinely useful named dilemma, but the short-supervised-interaction basis means the design recommendations are hypotheses, not validated results. read the letter →

arxiv 2504.18932 v1 pith:XFMGYLOO submitted 2025-04-26 cs.HC cs.AI

Dong Whi Yoo , Jiayue Melissa Shi , Violeta J. Rodriguez , Koustuv Saha This is my paper · ORCID
classification cs.HCcs.AI
keywords LLMmentalhealthdepressionchatbotconversationalagentethicsharmsself-management
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the harms of LLM mental health chatbots can be anticipated by asking people with lived experience of depression what they value, rather than relying only on expert opinion. It reports that 17 participants who interacted with a GPT-4o chatbot called Zenny in four self-management scenarios prioritized five values: informational support, emotional support, personalization, privacy, and crisis management. From those values the authors derive a mapping to potential harms and a set of design recommendations, including a harm mitigation checklist. This matters because public enthusiasm for AI chatbots is ahead of evidence about their risks, and a values-based map offers a concrete way to design for safety.

What carries the argument

The central object is Zenny, a GPT-4o-based technology probe embedded in a one-hour scenario-based interview. The mechanism is a value-harms mapping built from reflexive thematic analysis of chat logs, interview transcripts, and notes: participants' expressed values are treated as the standard against which potential harms are identified. The analysis also surfaces the personalization-privacy dilemma, the tension where tailored advice requires more sensitive disclosure, as the core dynamic designers must resolve.

What would settle it

A longitudinal field trial in which people use a mental health chatbot in their daily lives for several weeks, with crisis episodes and privacy incidents logged, would settle the claim: if reported harms do not track the five values, or if new values emerge that change the mapping, the central claim is weakened.

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Extended reading notes

Core claim

The paper's central claim is that the harms of LLM-based mental health chatbots can be mapped to values held by people with lived experience of depression, and that this mapping yields design guidance. In interviews built around interactions with Zenny, a GPT-4o chatbot used as a technology probe, participants prioritized five values: informational support, emotional support, personalization, privacy, and crisis management. The authors argue that inaccurate or inapplicable advice, over-reliance on chatbot emotional support, the personalization-privacy dilemma, and inadequate crisis handling are best understood as threats to these values, and they offer design recommendations plus a harm mitigation checklist to address them.

Load-bearing premise

The findings assume that what people value and fear during short, scripted, one-hour chats with a chatbot in a supervised interview is what they would value and fear in real, long-term use of a mental health chatbot.

Editorial extensions

If this is right

  • Mental health chatbots should explicitly tell users that responses may be inaccurate and encourage cross-checking with clinicians or other sources.
  • Chatbots should ask follow-up questions about constraints and preferences before giving advice, so suggestions are contextually applicable rather than generic.
  • Emotional-support features should be paired with prompts and scaffolding that steer users toward human support networks, reducing over-reliance.
  • Privacy controls should let users see and delete what the chatbot stores and infers, because users already obscure their queries to protect themselves.
  • Crisis management should be designed in from the start: clear limitations, referral to resources like 988, and governance oversight.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: If the value-harms mapping generalizes, showing users a visible profile of what the chatbot has inferred about them could be tested as a way to rebuild trust and reduce the privacy harm from invisible inference.
  • Editorial inference: The personalization-privacy dilemma implies a measurable trade-off between the amount of sensitive information disclosed and the relevance of advice, so interventions could be evaluated by how much they shift that trade-off in the user's favor.
  • Editorial inference: A longer-term deployment might surface additional values, such as continuity or trust calibration, because one-hour interactions cannot capture how users handle repeated use, memory, and broken advice over weeks.
  • Editorial inference: The same value-harms mapping approach could be extended to other mental health conditions or to comorbid populations, but only if the values are re-elicited rather than assumed to transfer.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper reports a qualitative interview study in which 17 U.S. adults with clinician-diagnosed depression and prior experience using AI chatbots interacted with Zenny, a GPT-4o-based technology probe, during one-hour remote interviews. Participants worked through two to four scripted self-management scenarios derived from van Grieken et al.'s taxonomy (goal setting, discussing depression with trusted others, leaving the house, and finding a new therapist) and were also invited to ask their own questions. Using reflexive thematic analysis of interview transcripts, chat logs, and notes, the authors derive five values: informational support, emotional support, personalization, privacy, and crisis management. They then map these values to potential harms and design recommendations in Table 4 and claim to contribute a harm mitigation checklist. The central claim is that harms of LLM-based mental health chatbots can be anticipated by grounding them in the values of people with lived experience of depression.

Significance. If the value-harms mapping is accepted as valid for real-world use, the paper is a useful value-sensitive design contribution that grounds harm anticipation in lived experience rather than only expert speculation. The study has genuine strengths: an explicit positionality statement, IRB-approved safety measures, scenario prompts based on patient-centered literature, an open 'ask your own question' channel, and a research team that includes a clinical psychologist. The identified personalization-privacy dilemma, supported by participant quotes about privacy-preserving tactics, is a substantive and actionable tension that deserves attention. However, the small self-selected sample, the short supervised probe context, and the intentional exclusion of crisis scenarios mean the empirical reach is narrower than the title and design recommendations suggest; the value-harms mapping is best read as a hypothesis-generating account rather than a validated basis for deployment decisions.

major comments (3)
  1. [§3.1, §3.1.1, §4, §5.4, Table 4] The values that anchor the central mapping were elicited in a one-hour, researcher-supervised interview in which the scenario prompts explicitly directed participants to request information (Table 1 tasks all ask for brainstorming, advice, or tips). The paper acknowledges in §5.4 that long-term interactions were not addressed, yet Table 4 and §5.2 issue recommendations for deployed systems, including long-term scaffolding to encourage human connection and crisis governance, that require the unstated premise that values from short, scripted, supervised interactions transfer to private, longitudinal use. The crisis-management value is especially thin in evidence: §4 states that crisis scenarios were intentionally excluded, the value is reported without a single participant quote, and the crisis harms row in Table 4 rests on hypothetical concerns rather than probe behavior. Please either reframe the contribution as 'values expressed in hypothetical supervised interactions' or provide a dedicated transferability argument, for example by triangulating the probe-based values with participants' prior real-world chatbot experiences.
  2. [Abstract, §1, §2.1, §5.2] The Abstract and Section 1 announce 'a harm mitigation checklist,' and Section 2.1 repeats this as a contribution, but no checklist is actually presented anywhere in the manuscript; Table 4 is a mapping of values, harms, and design recommendations, not a checklist with discrete actionable items. This claimed artifact should either be added as a distinct table or section, or the contribution should be corrected to say 'design recommendations' rather than 'harm mitigation checklist.'
  3. [§3.2, §3.4, §3.5, Table 2] The empirical basis for the five-value taxonomy is a sample of 17 volunteers from a single U.S. registry, all of whom had already used AI chatbots more than once, and the paper does not report how many of the four scenarios each participant completed or which scenarios were skipped. Because several participants could not complete all scenarios and the prompts strongly invoke informational support, the prominence of that value may be partly an artifact of task design and completion rates. The first author led the deductive coding (Section 3.5), and although this is consistent with reflexive thematic analysis, the absence of any reported codebook, coding excerpt, or scenario-completion breakdown makes it difficult to assess how the five values were consolidated. Please include a scenario-completion table and a short illustrative excerpt of the coding structure to strengthen the transferability claim.
minor comments (5)
  1. [§3.5, §4] There are several typos: 'expereinces' appears in §3.5, 'convinient' appears in §3.5, and 'the the' appears in §4 near 'Considering the the emerging use.'
  2. [Figure 1 caption] The Figure 1 caption says the probe is 'built with GPT-4,' but the Abstract, Section 3.1.2, and Section 6 say GPT-4o; these should be made consistent.
  3. [Reference [1]] Reference [1] has a malformed URL: 'https://https://platform.openai.com/docs/models/.' Remove the duplicate scheme.
  4. [§3.4, references [122] and [123]] Section 3.4 says the scenarios were based on patient perspectives on depression self-management [122], but reference [122] is van Berkel et al. on experience sampling; the intended source appears to be van Grieken et al. [123] (and [124]).
  5. [§2.3, reference [113]] The author name 'Søgaard Neilsen' is misspelled; the cited work by Søgaard Nielsen and Wilson should be spelled 'Søgaard Nielsen.'

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the value-harms mapping is built from primary interview and chat data, with self-citations limited to background framing.

full rationale

The paper's central claims—five values of people with lived experience of depression and a mapping of those values to harms and design recommendations—are derived from a qualitative empirical study, not from an analytic derivation that collapses into its inputs. The derivation chain is: participants interacted with a GPT-4o-based technology probe in scenario-based interviews (Section 3), and the authors performed reflexive thematic analysis on interview transcripts, interviewer notes, and chat histories (Section 3.5). The reported values (informational support, emotional support, personalization, privacy, crisis management) are presented as findings from that analysis rather than as predictions or as conclusions forced by a fitted parameter. No equation is fitted and then renamed as a prediction; no uniqueness theorem is imported from prior work; no load-bearing premise reduces to a self-citation. The self-citations in the paper (e.g., refs. 103, 137, 139, 141) appear in introductory and related-work framing and do not establish the empirical findings. The scenario design does draw on an external prior framework, van Grieken et al. [123], which shapes which situations participants encountered; this is a methodological choice, not a circularity, and the paper does not claim that the values were logically entailed by those scenarios. The acknowledged limitation in Section 5.4—that the study did not directly address long-term interactions—concerns external validity or transferability, not circularity. Overall, the central contribution is a thematic synthesis of new empirical data, so the paper is not circular; the score of 2 reflects only the presence of minor, non-load-bearing self-citations in the framing.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numeric parameters are fitted and no experimental constants are tuned; this is an interview study. GPT-4o's generation settings are not reported, but they are configuration choices, not fitted scientific parameters. No new theoretical entities are introduced: Zenny is a software probe, not an invented explanatory entity, and the five values are empirical coding categories rather than postulated constructs.

assumptions (4)
  • domain assumption Participants' self-reported clinician diagnoses are accurate.
    Section 3.2: inclusion is based on self-report that a clinician diagnosed MDD; comorbid conditions and years since diagnosis are also self-reported. Misdiagnosis or inaccurate self-report would change the composition of the lived-experience sample.
  • domain assumption Short, scripted, one-hour interactions with a probe elicit values similar to real-world chatbot use.
    Sections 3.1 and 3.4: Zenny is used only inside a supervised interview, and participants are prompted with scenarios rather than their own situations. Section 5.4 acknowledges there is no long-term or field deployment, so the transferability premise is unverified.
  • domain assumption The four selected scenarios cover the relevant AI chatbot self-management space.
    Section 3.1.1: the authors chose four of van Grieken et al.'s eight self-management themes because they were 'most relevant to AI chatbots,' a judgment call that shapes which values can emerge.
  • domain assumption Automated transcription plus first-author review preserves the nuance needed for thematic analysis.
    Section 3.5: Otter.ai transcripts were reviewed against audio by the first author; subtle emotional and conversational nuance can still be lost or interpreted through a single coder's lens.

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Cite this review

Pith. "Pith review of AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression." pith.science (2026). https://pith.science/paper/XFMGYLOO

@misc{pith2026250418932,
  author       = {Pith},
  title        = {Pith review of: AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XFMGYLOO}},
  note         = {Machine review of arXiv:2504.18932}
}
read the original abstract

Recent advancements in LLMs enable chatbots to interact with individuals on a range of queries, including sensitive mental health contexts. Despite uncertainties about their effectiveness and reliability, the development of LLMs in these areas is growing, potentially leading to harms. To better identify and mitigate these harms, it is critical to understand how the values of people with lived experiences relate to the harms. In this study, we developed a technology probe, a GPT-4o based chatbot called Zenny, enabling participants to engage with depression self-management scenarios informed by previous research. We used Zenny to interview 17 individuals with lived experiences of depression. Our thematic analysis revealed key values: informational support, emotional support, personalization, privacy, and crisis management. This work explores the relationship between lived experience values, potential harms, and design recommendations for mental health AI chatbots, aiming to enhance self-management support while minimizing risks.

Figures

Figures reproduced from arXiv: 2504.18932 by the authors.

Figure 1
Figure 1. Screenshots of the technology probe used in our interview study. The technology probe consists of a chatbot called [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.