The method efficiently identified test candidates aligned with stakeholder preferences, demonstrating superior coverage and optimal candidate generation compared to baseline approaches.
Bridging Objective Metrics and Subjective Values
Artificial intelligence (AI)-enabled autonomous systems are increasingly deployed in high-stakes domains like energy distribution and disaster management, yet their ethical evaluation remains challenging due to the lack of standardized metrics, evolving user-dependent values, and the high cost of real-world testing.
Prior work has largely focused on either rigid rule-based guidelines that lack actionable specificity or purely preference-based methods that assume abundant simulation budgets. Existing approaches fail to unify objective metrics with subjective stakeholder concerns under realistic resource constraints. This paper addressed that gap by introducing SEED-SET, a sample-efficient framework that integrates both objective evaluations and subjective stakeholder preferences through hierarchical Bayesian modeling to enable adaptive, scalable ethical benchmarking of autonomous systems.
A Scalable Framework for System-Level Ethical Evaluation
The paper formulated system-level ethical testing as a sample-constrained inference problem over an unknown ethical compliance function that integrates objective metrics with subjective stakeholder values. Given a black-box autonomous system, the goal was to evaluate its ethical alignment by querying it in various scenarios, collecting objective outcomes, and estimating compliance under a limited testing budget.
This formulation explicitly acknowledged three core challenges, namely, ethical criteria are multi-faceted and hierarchical, evaluation is expensive, and both the parameter space and human judgments contain significant uncertainty.
SEED-SET is a variational Bayesian experimental design framework built on three interconnected components. First, a hierarchical variational GP (HVGP) models the ethical landscape in two stages. An objective GP maps scenario parameters to measurable outcomes (such as cost, resilience), while a subjective GP learns stakeholder preferences over these outcomes through pairwise comparisons. This decomposition enhances interpretability and data efficiency. Second, a novel nested acquisition strategy guides adaptive testing by balancing exploration of uncertain objective and subjective spaces with exploitation of regions aligned with user preferences.
Third, to mitigate the cost of human annotation, SEED-SET employs large language models (LLMs) as proxy evaluators, using structured prompts that combine task context, objective metric comparisons, and stakeholder-specific criteria to generate reliable preference labels. Collectively, this approach enables scalable, sample-efficient ethical benchmarking of autonomous systems under realistic resource constraints.
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Validating Hierarchical Bayesian Design for Ethical Autonomy Testing
SEED-SET was evaluated across three case studies, namely, power grid resource allocation, autonomous fire rescue, and optimal routing, to test its scalability and sample efficiency in ethical benchmarking. Using generative pre-trained transformers (GPT)-4o as a proxy evaluator, they compare the proposed HVGP against several baselines, including random sampling, single GP, and version space active learning methods.
Results demonstrate that SEED-SET consistently achieves higher preference scores and better coverage of high-dimensional search spaces. Notably, while the single GP performs adequately on low-dimensional problems like the 5-Bus power network, it fails on the 40-dimensional 30-Bus case, whereas HVGP's hierarchical decomposition and novel acquisition strategy enable efficient exploration.
Ablation studies confirm that the full acquisition function, combining two mutual information terms with a preference exploitation term, outperforms variants lacking exploration or exploitation components. Additional analyses validate the use of handcrafted preference scores via TrueSkill rankings, demonstrate robustness to different LLM configurations, and show adaptability to multiple stakeholder preferences.
Scalability to extremely large datasets beyond tens of thousands of observations remains challenging, though stochastic variational inference could address this. The current stationary kernel assumption may be restrictive for systems with varying operational regimes, suggesting future extensions with non-stationary or deep GP.
The framework also requires complete a priori knowledge of objective metrics, which may not always hold in practice. Finally, while LLM proxies reduce annotation costs, their judgments remain sensitive to prompt design and require ongoing alignment with human values.
Unifying Objective Metrics and Subjective Values for Ethical AI
SEED-SET offers a principled and scalable approach to the ethical benchmarking of autonomous systems by unifying objective performance metrics with subjective stakeholder values through hierarchical Bayesian modeling. Its novel acquisition strategy efficiently balances exploration and exploitation under realistic resource constraints, while LLM-based proxy evaluators reduce reliance on costly human annotation.
Across power grid management, fire rescue, and routing tasks, SEED-SET consistently outperformed baselines in preference alignment and search space coverage, demonstrating robust adaptability to diverse stakeholder criteria. Although challenges remain in scaling to massive datasets and ensuring LLM alignment with human values, the framework establishes a strong foundation for interpretable, sample-efficient ethical evaluation in high-stakes AI applications.
Journal Reference
Zewe, A. (2026, April). Evaluating the ethics of autonomous systems. MIT News | Massachusetts Institute of Technology. https://news.mit.edu/2026/evaluating-autonomous-systems-ethics-0402
Parashar, A., Li, Y., Yu, E. Y., Chen, F., Neidhoefer, J., Upadhyay, D., & Fan, C. (2026). SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing. Openreview.net. https://openreview.net/forum?id=lfsjVdi72l
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