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EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI

Sai Kartheek Reddy Kasu

arXiv:2509.11648v1cs.CLcs.AIcs.CY

TL;DR

Existing benchmarks do not adequately capture the ethical dilemmas specific to mental-health practice. EthicsMH introduces a structured pilot dataset of 125 scenarios; it is intended as a tractable setting for examining whether models can weigh ethical trade-offs before large-scale deployment.

  • Problem

    Existing benchmarks and datasets miss nuanced ethical dilemmas in mental-health settings, where autonomy, beneficence, confidentiality, and bias intersect.

    Source: §1 · p. 1

  • Method

    A human-in-the-loop process produced scenarios with structured dilemmas, reasoning, stakeholder perspectives, and implications.

    Source: §5 · p. 4, §10 · p. 9

  • Results

    The 125-scenario pilot is balanced across five ethical subcategories and includes decision options, expert-aligned reasoning, expected model behavior, impact statements, and stakeholder viewpoints.

    Source: §5 · p. 4

  • Takeaways & Limitations

    EthicsMH offers a tractable setting for exploring whether models can identify and weigh ethical trade-offs before large-scale deployment.

    Source: §6 · p. 7

    Its 125-scenario scale precludes statistical generalizability or robust benchmarking, limiting it to a pilot resource.

    Source: §9 · p. 9

Abstract

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The deployment of large language models (LLMs) in mental health and other sensitive domains raises urgent questions about ethical reasoning, fairness, and responsible alignment. Yet, existing benchmarks for moral and clinical decision-making do not adequately capture the unique ethical dilemmas encountered in mental health practice, where confidentiality, autonomy, beneficence, and bias frequently intersect. To address this gap, we introduce Ethical Reasoning in Mental Health (EthicsMH), a pilot dataset of 125 scenarios designed to evaluate how AI systems navigate ethically charged situations in therapeutic and psychiatric contexts. Each scenario is enriched with structured fields, including multiple decision options, expert-aligned reasoning, expected model behavior, real-world impact, and multi-stakeholder viewpoints. This structure enables evaluation not only of decision accuracy but also of explanation quality and alignment with professional norms. Although modest in scale and developed with model-assisted generation, EthicsMH establishes a task framework that bridges AI ethics and mental health decision-making. By releasing this dataset, we aim to provide a seed resource that can be expanded through community and expert contributions, fostering the development of AI systems capable of responsibly handling some of society's most delicate decisions.

1 Introduction

Mental health AI applications are expanding, but existing benchmarks often miss ethical dilemmas specific to therapeutic settings. EthicsMH addresses this gap with a pilot dataset of structured, multi-stakeholder scenarios.

1 Introduction

  • Mental-health AI applications include screening, conversational support, patient-interaction summaries, and clinician decision support.The paper notes potential benefits for accessibility, clinician workload, and personalized care recommendations.Source: §1 · p. 1
  • Existing benchmarks often omit nuanced mental-health dilemmas involving autonomy, beneficence, confidentiality, and demographic bias.The paper argues that ethical-reasoning errors can affect patient safety, trust, and societal impact.Source: §1 · p. 1
  • EthicsMH introduces a pilot dataset of 125 ethically challenging mental-health scenarios with structured decision options and expert-aligned reasoning.Its fields also cover expected model behavior, real-world impact, and multiple stakeholder viewpoints.Source: §1 · pp. 1–2
  • The dataset’s multi-view annotations support research on fairness, ethical alignment, and sensitivity to patient-centered concerns.The authors present it as a proof of concept for small-scale ethical-reasoning datasets in sensitive domains.Source: §1 · p. 2

2 Related Work

Prior mental-health AI datasets largely target task performance or dialogue, while general ethics benchmarks do not capture the domain’s specific dilemmas. EthicsMH is motivated by this gap in structured ethical reasoning.

2 Related Work

  • Existing mental-health AI work includes depression detection, suicidal-ideation risk identification, disorder classification, conversational support, and therapy-session summarization.The cited examples include Cohan et al. (2018), Yates et al. (2017), and Srivastava et al. (2025b).Source: §2 · p. 2
  • Many mental-health datasets focus on behavior annotation, trust or engagement modeling, or counseling-interaction summarization rather than structured ethical reasoning.The passage cites Srivastava et al. (2025a, 2022).Source: §2 · p. 2
  • Behavioral and dialogue datasets omit trade-offs such as autonomy versus beneficence, confidentiality, and bias, while general ethics benchmarks lack mental-health context.ETHICS (Hendrycks et al., 2020) and Scruples (Lourie et al., 2021) address moral dilemmas but remain detached from mental-health practice.Source: §2 · p. 3

3 Comparative Analysis with Existing Benchmarks

EthicsMH is positioned as a complementary benchmark combining mental-health specificity with stakeholder diversity, structured reasoning, bias evaluation, and real-world impact.

3 Comparative Analysis with Existing Benchmarks

  • EthicsMH centers on therapy and psychiatric dilemmas, unlike domain-general ETHICS or broader clinical MedEthicEval; MentalChat16K emphasizes counseling dialogue.The comparison distinguishes structured ethical dilemmas from conversational support.Source: §3 · p. 3
  • EthicsMH encodes viewpoints from patients, therapists, parents, legal authorities, and cultural lenses, foregrounding multiple actors in mental-health decisions.ETHICS and MedEthicEval mainly focus on single decision-makers or expert judgments, while MentalChat16K does not systematically capture multiple stakeholders.Source: §3 · p. 3
  • Each EthicsMH sample links a scenario and response options to reasoning tasks, expected reasoning, model behavior, and real-world impact.This schema supports analysis of both decisions and their justifications.Source: §3 · p. 3
  • EthicsMH explicitly includes race- and gender-bias subcategories, whereas the compared benchmarks lack demographic-specific bias evaluation.ETHICS treats fairness generally, MedEthicEval touches on it indirectly, and MentalChat16K lacks explicit bias evaluation.Source: §3 · p. 3
  • EthicsMH’s real-world impact field records societal and therapeutic implications that the compared benchmarks annotate only minimally or not at all.The other resources focus more on immediate choices or conversational quality.Source: §3 · p. 3
  • Existing benchmarks advance moral reasoning and clinical ethics, but none combine domain specificity, stakeholder diversity, structured reasoning, explicit bias evaluation, and real-world impact as EthicsMH does.The authors position EthicsMH as a complementary resource addressing a gap in ethical-reasoning datasets.Source: §3 · p. 3

4 Challenges in Ethical Reasoning for Mental Health AI

Ethical reasoning in mental-health AI must account for social, cultural, and legal contexts, competing stakeholder values, consequential harms, and bias. EthicsMH responds with scenarios and structured fields for these dimensions.

4 Challenges in Ethical Reasoning for Mental Health AI

  • Mental-health ethics differs from standard diagnostic or treatment tasks because decisions arise within complex social, cultural, and legal contexts.These settings require nuanced ethical judgment rather than only clinical accuracy.Source: §4 · p. 4
  • Ethical norms vary across cultures, jurisdictions, and care settings, so recommendations trained in one context may be inappropriate or harmful elsewhere.The passage gives mandatory reporting and family involvement as examples of variation.Source: §4 · p. 4
  • Decisions can involve patients, clinicians, parents, caregivers, and legal authorities whose values around autonomy, safety, confidentiality, and beneficence may conflict.The passage says capturing these perspectives is difficult for systems optimized for single-output predictions.Source: §4 · p. 4
  • Ethical misjudgments can harm vulnerable populations through loss of life, eroded trust, or reinforced structural bias, while systems risk amplifying race-, gender-, and age-related bias.Bias detection is challenging where practitioner and cultural judgments vary.Source: §4 · p. 4
  • EthicsMH addresses these challenges with realistic scenarios, structured decision options, professional reasoning, and multiple stakeholder perspectives.The proposed benchmark moves beyond surface-level predictions to encode dilemmas, reasoning processes, and stakeholder impacts.Source: §4 · p. 4

5 Dataset Description

EthicsMH is a 125-scenario pilot dataset with structured annotations for evaluating ethical dilemmas in mental health practice. It balances five ethical subcategories and was developed through iterative expert review of model-generated drafts.

Dataset Description

  • Each record combines a contextual vignette, decision options, an explicit reasoning task, expert-aligned expected reasoning, recommended model behavior, real-world impact, and stakeholder viewpoints.The multi-view annotations support evaluation of decision selection, explanation quality, and alignment with professional norms.Source: §5 · pp. 4–6
  • Each of the five ethical subcategories contains 25 scenarios, evenly covering confidentiality, bias, and autonomy-beneficence tensions in adult and minor contexts.These categories were selected in consultation with mental health professionals and reflect issues described as prevalent in practice but underexplored in computational ethics.Source: §5 · pp. 4–5, Table 2 · p. 5
  • The pilot is intended as a focused testbed for defining tasks, iterating on evaluation protocols, and validating prompts and annotations under expert oversight before larger-scale annotation.The authors aim to seed reproducible investigations and community-driven expansion with expert validation.Source: §5 · p. 5
  • ChatGPT drafts were iteratively reviewed by a mental health professional for realism, well-posed ethical trade-offs, and relevant stakeholder perspectives, then refined until coherent and aligned with professional standards.Review feedback addressed issues including oversimplification, implausible reasoning, and missing viewpoints.Source: §5 · p. 6
  • Table 3 summarizes minimum, mean, and maximum character counts across fields; scenarios, reasoning tasks, and expected reasoning are substantial, while viewpoints and options are comparatively longer.The authors link this detail to studying ethical reasoning across decision options, professional standards, and real-world implications.Source: Table 3 · p. 6, §5 · p. 6

6 Potential Use Cases

The paper describes uses for EthicsMH in diagnosing model tendencies, pre-deployment testing, ethical-reasoning prototyping, safeguard design, and planning larger expert-validated corpora. These applications use its structured scenarios to examine model decisions and rationales against expected reasoning and stakeholder perspectives.

Prototyping ethical-reasoning capabilities

  • Developers can use compact cases to probe whether models attend to stakeholder harms, legal thresholds, and bias signals, and compare justifications under different prompting regimes.The dataset's expected reasoning and viewpoints support interpretable diagnostic comparisons rather than opaque accuracy numbers.Source: §6 · pp. 6–7

Supporting early-stage system design and safeguards

  • Designers can observe risky tendencies in candidate systems and use them to shape safeguards such as risk-checking prompts, confidentiality filters, and escalation policies.The four distinct options and stakeholder viewpoints also let teams examine whether safeguards preserve nuance while preventing unsafe behaviors.Source: §6 · p. 7

Blueprint for larger, expert-validated corpora

  • EthicsMH offers a procedural blueprint for larger corpora through its reusable schema, human-in-the-loop review workflow, quality checks, and expert-feedback patterns.Teams can adapt these materials to design annotation tasks, assess agreement for reasoning labels, and estimate annotation budgets.Source: §6 · p. 7

Diagnostic evaluation of model tendencies and failure modes

  • Researchers can compare model decisions and explanations with expected reasoning and stakeholder viewpoints to identify patterns such as neglected perspectives or paternalistic solutions.The resulting diagnostics can inform targeted training signals, constraint modules, or policy checks for omissions.Source: §6 · pp. 7–8

Pre-deployment stress-testing and risk assessment

  • Teams can use EthicsMH scenarios in acceptance testing and clinician red-team sessions to document ethically problematic guidance and inform triage thresholds, human-review triggers, and rollout safeguards.The authors describe it as a pilot stress-testing resource for risk assessments and governance decisions.Source: §6 · p. 8

7 Ethical Considerations

EthicsMH is intended strictly for research, not clinical, diagnostic, or commercial use or as a substitute for professional mental health care. Its scenarios are synthetic and contain no identifiable patient data; self-harm and suicidal-ideation scenarios require particular caution in deployment.

Ethical Considerations

  • EthicsMH is for research purposes only, not clinical, diagnostic, or commercial use, and is not a substitute for professional mental health care.The scenarios are synthetic and contain no identifiable patient data.Source: §7 · p. 8
  • Models trained on self-harm or suicidal-ideation scenarios should not be deployed in patient-facing systems without clinical oversight and appropriate safety mitigations.Source: §7 · p. 8

8 Broader Impact

EthicsMH is a small-scale pilot that offers a structured starting point for examining how model decisions align with professional reasoning and stakeholder perspectives. Its human-in-the-loop development also illustrates a path toward larger, more diverse datasets.

Broader Impact

  • As a small-scale pilot, EthicsMH provides a structured starting point for examining model decisions and their alignment with professional reasoning and stakeholder perspectives.The authors frame this as a way to address risks from mental health technologies insensitive to ethical dilemmas, including undermined therapeutic relationships, bias, or unintended harm.Source: §8 · p. 8
  • The work highlights the feasibility of combining generative models with expert validation to create ethically grounded resources.Source: §8 · p. 8
  • The authors suggest that larger, more diverse datasets could capture cultural, regional, and institutional variations in ethical reasoning and inform more equitable, context-sensitive, and socially responsible systems.Source: §8 · pp. 8–9

9 Limitations

EthicsMH is a pilot resource whose small scale, synthetic generation, and limited cultural coverage constrain its use; its scope and lack of evaluations also limit what it can support.

9 Limitations

  • With 125 scenarios, EthicsMH cannot support statistical generalizability or robust benchmarking.Cases are synthetically generated and iteratively reviewed by a mental health expert, which mitigates but does not eliminate risks of oversimplification, artificial phrasing, or subtle biases.Source: §9 · p. 9
  • Its limited cultural and regional perspective leaves ethical norms shaped by other societal, legal, and local contexts only partially represented.The authors identify broader cultural coverage as a direction for future work.Source: §9 · p. 9
  • The dataset covers five core subcategories but omits multi-patient trade-offs, institutional dilemmas, and resource-allocation conflicts.It has no baseline model evaluations, leaving performance assessment and application-specific adaptation to the community.Source: §9 · p. 9
  • EthicsMH is a research resource, not a clinical guideline, and must not be used for clinical decision-making or diagnosis.Source: §9 · p. 9

10 Conclusion and Future Work

EthicsMH provides a human-in-the-loop pilot dataset of 125 scenarios for examining AI reasoning in ethically sensitive mental-health contexts. The authors frame it as a foundation for further evaluation work and plan to expand its diversity and scale.

10 Conclusion and Future Work

  • EthicsMH encodes 125 scenarios across autonomy, beneficence, confidentiality, and fairness, with structured fields for dilemmas, reasoning, stakeholder perspectives, and implications.Source: §10 · p. 9
  • The dataset is intended as a foundation for exploring model reasoning in complex therapeutic contexts and developing evaluation methods that prioritize ethical soundness alongside technical performance.Source: §10 · p. 9
  • Future work will expand scenario diversity and scale, deepen cultural representation, and develop metrics better aligned with professional mental-health standards.Source: §10 · p. 9

Appendix A: Example Scenarios from EthicsMH

The examples illustrate mental-health scenarios involving confidentiality, bias, and autonomy, alongside the structured fields used to express ethical trade-offs and expected reasoning. The accompanying templates describe an LLM drafting process followed by expert review and refinement.

Appendix A: Example Scenarios from EthicsMH

  • In the teen confidentiality scenario, the expected approach seeks consent to involve parents collaboratively, while maintaining trust and addressing safety concerns.The example also flags risks of absolute parental disclosure and hallucinated reporting laws.Source: Example 1: Confidentiality and Trust… · p. 11
  • For racial bias that underdiagnoses depression in Black patients, the preferred option is to delay deployment until more diverse data improves fairness.The example warns that models may default to majority-based optimization without equity considerations.Source: Example 2: Bias in AI (Race) · pp. 11–12
  • The gender-bias example contrasts stress-management advice to women with career counseling to men reporting identical burnout symptoms; retraining to neutralize unjustified differences is preferred.The example identifies rationalizing harmful disparities as personalization as a potential model pitfall.Source: Example 3: Bias in AI (Gender) · p. 12
  • For an adult refusing medication, the expected approach initially involves family support for voluntary treatment, with involuntary treatment justified if risk remains unmanageable.The example flags rigid prioritization of autonomy or safety and neglect of procedural rights as model pitfalls.Source: Example 4: Autonomy vs. Beneficence… · p. 13
  • For a minor refusing hospitalization amid critical anorexia-related risks, the expected approach seeks a time-limited stay with partial agreement, while retaining a safety-based alternative if risk remains extreme.The example warns against both automatically overriding minors and overlooking clinical urgency.Source: Example 5: Autonomy vs. Beneficence… · pp. 13–14

Appendix B: Prompt Templates for Dataset Construction

  • The construction templates prompt LLMs to draft realistic dilemmas and return scenarios with options, reasoning, model behavior, impacts, and stakeholder viewpoints in JSON.Source: Appendix B · p. 14
  • A mental-health professional reviews each draft for realism, meaningful trade-offs, and diverse perspectives; refinement prompts incorporate feedback and strengthen cultural sensitivity and professional alignment.Source: Appendix B · p. 14, Step 3: Refinement Prompt… · p. 14
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