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

arXiv: https://arxiv.org/abs/2509.11648 · HTML: https://mlsanity.com/paper/2509.11648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 9 of 9)

- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes](https://mlsanity.com/paper/2008.09094/) (2020)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance](https://mlsanity.com/paper/2503.13509/) (2025)
- [Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering](https://mlsanity.com/paper/2206.03886/) (2022)
- [MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics](https://mlsanity.com/paper/2503.02374/) (2025)
- [Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities](https://mlsanity.com/paper/2309.01618/) (2023)
- [Artificial Intelligence and the Future of Psychiatry: Qualitative Findings from a Global Physician Survey](https://mlsanity.com/paper/1910.09956/) (2019)

## Built on by (most cited first, 1 of 1)

- [MiraMind: Benchmarking Reliable Mental Health Reasoning beyond Answer Accuracy](https://mlsanity.com/paper/2512.09636/) (2025)

## Abstract (arXiv)

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.