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

Current AI benchmarks fail to capture mental health's unique ethical challenges: Existing moral and clinical decision-making benchmarks don't adequately address the specific ethical dilemmas in mental health practice, where confidentiality, autonomy, beneficence, and bias frequently intersect—highlighting a critical gap in how we evaluate AI systems for sensitive domains.

EthicsMH provides a structured framework for testing ethical reasoning: The pilot dataset of 125 scenarios goes beyond simple right/wrong answers by evaluating explanation quality, alignment with professional norms, and multi-stakeholder perspectives, offering a more comprehensive way to assess whether AI systems can handle ethically complex mental health situations responsibly.

AI systems need to demonstrate more than accurate decisions: The benchmark emphasizes that responsible mental health AI must not only make correct choices but also provide sound reasoning that aligns with professional standards—suggesting that how an AI explains its decision matters as much as the decision itself.

This is an early-stage resource designed for community expansion: With only 125 scenarios and model-assisted generation, EthicsMH is intentionally positioned as a "seed resource" that requires expert and community contributions to grow, acknowledging that no single dataset can capture all ethical nuances in mental health practice.

Deploying LLMs in mental health demands urgent ethical scrutiny: The paper underscores that the widespread use of large language models in mental health and other sensitive domains raises pressing questions about fairness and alignment that current evaluation methods don't adequately address.