When Robots Say No: The Empathic Ethical Disobedience Benchmark

TL;DR

The paper introduces the Empathic Ethical Disobedience (EED) Gym, a standardized benchmark for evaluating robot refusal behaviors that balance safety and social trust, incorporating diverse scenarios and trust models.

Contribution

It presents the EED Gym, a novel testbed for systematically assessing ethical disobedience in robots through multiple scenarios, metrics, and grounded trust models.

Findings

Explanatory refusals help maintain trust.

Action masking prevents unsafe compliance.

Constructive and empathic styles influence trustworthiness.

Abstract

Robots must balance compliance with safety and social expectations as blind obedience can cause harm, while over-refusal erodes trust. Existing safe reinforcement learning (RL) benchmarks emphasize physical hazards, while human-robot interaction trust studies are small-scale and hard to reproduce. We present the Empathic Ethical Disobedience (EED) Gym, a standardized testbed that jointly evaluates refusal safety and social acceptability. Agents weigh risk, affect, and trust when choosing to comply, refuse (with or without explanation), clarify, or propose safer alternatives. EED Gym provides different scenarios, multiple persona profiles, and metrics for safety, calibration, and refusals, with trust and blame models grounded in a vignette study. Using EED Gym, we find that action masking eliminates unsafe compliance, while explanatory refusals help sustain trust. Constructive styles are rated most trustworthy, empathic styles -- most empathic, and safe RL methods improve robustness but also make agents more prone to overly cautious behavior. We release code, configurations, and reference policies to enable reproducible evaluation and systematic human-robot interaction research on refusal and trust. At submission time, we include an anonymized reproducibility package with code and configs, and we commit to open-sourcing the full repository after the paper is accepted.