EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI
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.
68c8c3ac733e345e52ac1e58
Sai Kartheek Reddy Kasu
claimed_verified
2025-09-16T09:42:43.840Z
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Sai Kartheek Reddy
user
UVSKKR
EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI
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.
EthicsMH is a dataset of 125 scenarios designed to evaluate AI systems' ethical reasoning in mental health contexts, focusing on decision accuracy, explanation quality, and alignment with professional norms.
large language models LLMs ethical reasoning fairness responsible alignment mental health ethical dilemmas confidentiality autonomy beneficence bias pilot dataset decision options expert-aligned reasoning expected model behavior real-world impact multi-stakeholder viewpoints AI ethics mental health decision-making