Ethical Refusal Scale: Guidelines for Autonomous Systems

• Ethical Refusal Scale is a framework that defines when and how autonomous systems decline requests based on ethical, legal, and societal principles.
• It integrates multiple ethical frameworks, including utilitarianism, Kantian ethics, and care ethics, to adjust refusals based on contextual risk and stakeholder perspectives.
• Empirical benchmarks from human-robot interactions and LLM content moderation validate its performance, ensuring a safe balance between refusal strictness and user trust.

An Ethical Refusal Scale defines, measures, and guides when and how autonomous systems—particularly robots and LLMs—should decline to perform certain actions or provide certain information due to ethical, safety, legal, or societal considerations. This concept encompasses not only the triggers for refusal but also the quality, transparency, and contextual appropriateness of the refusal behavior, integrating multiple ethical frameworks and empirical human judgment.

1. Conceptual Foundations and Theoretical Principles

The Ethical Refusal Scale arises from the observation that autonomous systems and LLMs often face situations requiring the choice to refuse, particularly when an action or response may violate ethical norms, explicit rules, or legal standards (e.g., International Humanitarian Law as in (2506.06391)). The core of the scale is to capture nuanced, context-sensitive gradations in refusal—going beyond a simple binary accept/decline mechanism.

A principled scale integrates:

• Multiple ethical frameworks (Utilitarianism, Kantian Ethics, Ethics of Care, Virtue Ethics, Rawlsian Justice) to ground decision-making in tested moral reasoning (2206.10727).
• Scenario sensitivity: Distinguishing high-risk (e.g., medical, legal) and low-risk (e.g., games or harmless queries) contexts and modulating the threshold and form of refusals accordingly.
• Stakeholder perspectives: Balancing expert doctrine, folk morality, and empirical user expectations to produce socially acceptable outcomes.
• Quantitative metrics: Employing formal statistics (e.g., McNemar’s test, regression coefficients) and discrete axes (risk, performance deficiency, context cues) for validation.

2. Taxonomies and Typologies of Refusal

Accurate measurement and operationalization rely on robust taxonomies. Refusals can be classified as (2412.16974):

• Should Not–Related: Motivated by ethics, law, policy, or system-level rules (e.g., illegality, privacy, NSFW, information hazards, intellectual property).
• Cannot–Related: Rooted in technical or epistemic limitations (lack of modality, insufficient skill, knowledge cutoff, missing context, invalid premise).

The taxonomy is explicitly non-mutually exclusive and exhaustive; refusals can reference multiple overlapping reasons. Mathematical modeling treats the assignment as a set-valued function: $c: (S, I, O) \rightarrow 2^{\mathcal{C}}$ with $\mathcal{C}$ being the set of refusal categories.

Recognizing and balancing these categories is essential for ensuring both safety (avoiding under-refusal) and usefulness (avoiding over-refusal), while supporting transparency and auditability.

3. Empirical Benchmarks and Evaluation Metrics

Multiple benchmarks and metrics have been developed to assess and calibrate ethical refusals:

• Human-robot interaction scenarios: Granular scenario-based surveys examine how various ethical doctrines and folk intuitions align or diverge in high/low-risk interactions (2206.10727).
• LLM content moderation datasets: Fine-tuned classifiers distinguish between ethical, technical, and ambiguous refusals and quantify the "refusal penalty" in terms of user satisfaction (e.g., win rates, OLS regression coefficients, as in (2501.03266, 2505.15365)).
• High-stakes action-forcing scenarios: Triage and medical law benchmarks measure not just the refusal rate, but also the consequences of overcaring (over-refusing) vs. undercaring (under-refusing), with careful attention to both best- and worst-case performance under adversarial prompts (2410.18991, 2410.19753).
• Refusal helpfulness: The proportion of refusals offering explicit legal/ethical rationale (clarity, transparency) is tracked, with system-level safety prompts shown to significantly increase helpfulness in IHL-based refusals (2506.06391).
• Statistical measures:
  • Refusal rate and helpfulness rate (as proportions of correct refusals or explanations).
  • Prompt classifier accuracy for refusal prediction (e.g., BERT: 75.9–96.5% for different tasks (2306.03423)).
  • Cluster separation metrics (e.g., GDV) for mechanistic analysis of refusal encoding within neural activations (2501.08145).
  • Multi-label agreement rates between classifiers, LLMs, and human annotators.

Typical formulas include: $$\text{Refusal Rate} = \frac{\text{Number of refusals}}{\text{Total IHL-violating prompts}} \times 100\%$$

$$\text{Abstention Precision} = \frac{N_5}{N_3 + N_5}$$

and related F1, recall, and error type breakdowns for nuanced scoring (2407.18418).

4. Mechanisms, Strategies, and Quality of Refusal

Refusal mechanisms range from formulaic denials to complex, context-aware explanations. Key distinctions include:

• Direct refusal: Briefly denies without elaboration; effective for safety but often harms user satisfaction (2506.00195).
• Explanation-based refusal: Declines while referencing legal or ethical standards; increases user trust, clarifies model boundaries, deters adversarial probing (2506.06391).
• Partial compliance: Provides general or non-actionable information without full compliance, balancing safety and user experience; empirically shown to reduce negative perceptions by more than 50% compared to flat refusals (2506.00195).
• Rebuttal strategies: Explicitly counter harmful requests by upholding principles or contesting premises, shown to robustly suppress downstream unethical actions and "reason-based deception" (2406.19552).

The effectiveness and acceptability of refusal strategies are contingent on both user context (benign or harmful intent, sensitivity of request) and refusal phrasing (length, alignment, explanation) (2501.03266).

5. Alignment with Human Values and Normative Tensions

Ethical refusal cannot be separated from the tension between developer-imposed (LLM) norms and user or societal values. LLM-as-a-Judge (LaaJ) systems habitually judge ethical refusals as more desirable than do human users—a form of "moderation bias" (2505.15365). For ethical refusals, LLM-based ratings may award a win ratio up to four times higher than real users, especially for safety-centric refusals. This has direct implications:

• Over-optimization for AI-judge-based scales risks systematic misalignment with user expectations.
• Transparent, participatory, and contestable evaluation—where both automated and human perspectives are valued—is necessary for robust scale construction.

6. Challenges, Limitations, and Recommendations for Scale Development

Challenges for an effective Ethical Refusal Scale include:

• Contextual robustness: Blanket refusals can fail in high-stakes, action-forcing scenarios (e.g., triage) where refusing to act is itself unethical (2410.18991, 2410.19753).
• Vulnerability to adversarial prompts: Context perturbations or technical wording can degrade refusal quality and reverse error patterns, exposing hidden weaknesses in overfit or under-aligned models (2410.19753, 2506.06391).
• Balancing safety and user satisfaction: Refusals protecting against genuinely harmful requests must not unduly frustrate or alienate users in ambiguous cases (2501.03266, 2506.00195).
• Reward model misalignment: Current RMs undervalue contextually optimal strategies such as partial compliance, necessitating revision to better reflect nuanced human judgments (2506.00195).
• Explanatory clarity: Models should issue clear, context-specific, and legally or ethically grounded refusals, achievable via system-level safety prompts and explicit response design (2506.06391).

Recommendations include:

• Design evaluation protocols that stress-test models under adversarial and worst-case conditions, not just best-case or average-case utility (2410.19753).
• Structure benchmarks around real-world, unambiguous dilemmas validated by domain experts (2410.18991, 2410.19753).
• Prefer multi-criteria, multi-perspective approaches—balancing technical, legal, ethical, and user-derived measures—for grading refusal behaviors.
• Transparently report and audit moderation biases and normative assumptions, and integrate contestability and diversity into scale governance (2505.15365).

7. Future Directions

Emerging directions for research and deployment include:

• Automated, scalable benchmarking using synthetic and AI-generated real-world scenarios, with human-in-the-loop validation where required (2410.19753, 2412.16974).
• Meta-capability frameworks where abstention and refusal become cross-task, compositional skills, generalizable to new domains and contexts (2407.18418).
• Mechanistic interpretability of refusal features—mapping how and where neural activations encode refusal, improving robustness, and detecting alignment faking (2501.08145).
• Human-centered optimization of both LLM and RM behavior, ensuring partial compliance and explanation-based strategies are valued appropriately in training and deployment (2506.00195).
• Context-sensitive, law-grounded alignment for high-risk domains, leveraging system prompts and refusal explanation quality as axes for compliance auditing (2506.06391).

An Ethical Refusal Scale, to be effective, must be flexible, context-aware, empirically validated, and continually updated to reflect both societal norms and technical best practices. It serves as a foundational tool for the design, auditing, and governance of ethically reliable autonomous systems.