SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing
Authors: Anjali Parashar
Yingke Li
Eric Yang Yu
Fei Chen
James Neidhoefer
Devesh Upadhyay
Chuchu Fan
Venue: ICLR 2026 Poster (2026)
Keywords: Ethical AI
Bayesian Experimental Design
System-level Evaluation
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: We propose SEED-SET, a scalable Bayesian framework for ethical testing that aligns system metrics with evolving human values using hierarchical models and LLM proxies.

Abstract: As autonomous systems such as drones, become increasingly deployed in high-stakes, human-centric domains, it is critical to evaluate the ethical alignment since failure to do so imposes imminent danger to human lives, and long term bias in decision-making. Automated ethical benchmarking of these systems is understudied due to the lack of ubiquitous, well-defined metrics for evaluation, and stakeholder-specific subjectivity, which cannot be modeled analytically.  To address these challenges, we propose SEED-SET, a Bayesian experimental design framework that incorporates domain-specific objective evaluations, and subjective value judgments from stakeholders. SEED-SET models both evaluation types separately with hierarchical Gaussian Processes, and uses a novel acquisition strategy to propose interesting test candidates based on learnt qualitative preferences and objectives that align with the stakeholder preferences.
    We validate our approach for ethical benchmarking of autonomous agents on two applications and find our method to perform the best. Our method provides an interpretable and efficient trade-off between exploration and exploitation, by generating up to $2\times$ optimal test candidates compared to baselines, with $1.25\times$ improvement in coverage of high dimensional search spaces.

Published as a conference paper at ICLR 2026
SEED-SET: SCALABLE EVOLVING EXPERIMENTAL DESIGN FOR SYSTEM-LEVEL ETHICAL TESTING
Anjali Parashar' Yingke Li' Eric Yang Yu'
Fei Chen'
James Neidhoefer Devesh Upadhyay² Chuchu Fan "Laboratory for Information & Decision Systems (LIDS), MIT janjipp, yingkeli 11, eyyu, feic, , jimneid, chuchu) @mit edu devesh. upadhyay& (athhhyyyyaabbbing saabinc. com.
Saab Inc.
ABSTRACT
As autonomous systems such as drones, become increasingly deployed in high- stakes, human-centric domains, it is critical to evaluate the ethical alignment since failure to do imposes imminent danger to human lives, and long term bias in decision-making. Automated ethical benchmarking of these systems is understudied due to the lack of ubiquitous, well-defined metrics for evaluation, and stakeholder-specific subjectivity, which cannot be modeled analytically. To address these challenges, we propose SEED-SET, a Bayesian experimental design framework that incorporates domain-specific objective evaluations, and subjective value judgments from stakeholders. SEED-SET models both evaluation types separately with hierarchical Gaussian Processes, and uses a novel acquisition strategy to propose interesting test candidates based on learnt qualitative preferences and objectives that align with the stakeholder preferences. We validate our approach for ethical benchmarking of autonomous agents on two applications and find our method to perform the best. Our method provides interpretable and efficient trade-off between exploration and exploitation, by generating up to 2x optimal test candidates compared to baselines, with 1.25x improvement in coverage of high dimensional search spaces
INTRODUCTION
Artificial intelligence (Al)eenabled autonomous systems have seen increased deployment across wide range of high-staker domains, including automated energy distribution, disaster management (Battistuzzi al. (021T) Although such applications can bring significant social benefits (Maslej
et al.) [2025] Zeng et al. 2024; Weidinger et al.) [021] Birhane et al., 2024), they raise equally
urgent ethical concerns (Sovacool et al. 2016 Bhattacharya al. 2024 Amodei al. 2016 Jobin et al. 2019 Wang et al. 2025 Grabb et al. 2024 Palka 2023) across stakeholder groups. For
example, in the power grid resource allocation problem, energy distribution policies often prioritize higher-income areas during peak demand periods, leaving marginalized populations more vulnerable to outages (Fahmin et al. 2024 (Citikena et al. 2023 Cong et al., 2022). Such examples highlight three challenges of ethical evaluation in real-world autonomous systems:
Measuring ethical behavior is difficult. Standard ethical evaluation metrics such as fairness and social acceptability often lack bround-truth labels (Mittelstadt et al. 2016 Salaudeen et al. 2025
Reuel et al. 2024) Wallach et al. 2025).
Value alignment is dependee,,,, and evolving. Evaluation standards must quickly adapt to the growing capabilities of autonomous systems (Keswani et al. 2024 Tarsney al. 2024). Static evaluation standards such as test suites and benchmarks require persistent revisions. Additionally, wide range of ethical benchmarking problems user-specific and user evaluation be noisy.
Ethical evaluation of real-world platforms is expensive. Due resource constraints such budget, real-world systems require sample-effiche evaluation. Disproportionate access to large-scale human feedback across domains also imposes sample restrictions on stakeholders.
"Corresponding author
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SEED-SET
Prompt
Crlteria
Accondiig
poovrided criterta
$
LLM Fualuations
Voltage Fairness
Piiorny area
coverage
y/ y2
Objective evaluation
(e) System-Level Simulations
metrics (y)
[a]
Normer zelion
(b)
Normalization
Feature Space
Objective
Latent utility
Jadgementt
(2)
Objective
Subjective GPs (g:y
Desigh Acquisition
Bayeslan Experimental Design
Figure 1: SEED-SET Overview. Our framework integrates quantitative metrics learned using Objective GP (a) with user preferences learned via Subjective GP through pairwise elicitation (b). An LLM performs pairwise comparisons of scenario outcomes (c) inform the acquisition process (d), which generates pair of scenarios for evaluation, aligned with user-defined ethical criteria. These scenarios are then used for system-level simulations (e) in a sequential manner.
To address the first challenge, guidelines and standards for ethical behavior in Al systems have been proposed (Tabassi 2023 Winfield et al. 2021 ISO 2024 Chance al.) 2023). For example, NIST's
Al Risk Management Framework (AT RMF 1.0, 2023) that suggests high-level guidelines (e.g.. Govern, Map. Measure, Manage) to promote 'trustworthy AI'. Although these guidelines serve as useful heuristics for enforcing measurable ethical behavior, they are not sufficiently concrete for direct system-level testing. Some recent efforts in this direction have led automated evaluation tools. largely focusing on rule-based ethical benchmarking based solely on established guidelines (Reuel) al. 2022 Dennis et al., 2016), or preference-bar based methods using human feedback (Keswani et al., 2024 Liu et al. 2024). and often argue in favor of one over the other. Instead, we argue that in its most general form, ethical evaluation must incorporate objective feedback from existing guidelines, well stakeholder concerns.
Additionally, existing works assume abundant access to cheap simulations or expert annotations, leading to sample-extensive approaches based reinforcement learning (RL), reinforcement learning from human feedback (RLHF), or adaptive stress testing (Reuel et al. 2022 Dennis et al., 2016 Gao et al. 2024). Such assumptions restrict their applicability to real-world systems, underscoring the need for methods that unify both forms of evaluation under realistic data and resource constraints.
To address this, we propose Scalable Evolving Experimental Design for System-level Ethical Testing (SEED-SET), an evaluation methodology that benchmarks autonomous systems against both objective measurable metrics (e.g. fire damage to buildings) and subjective ethical metries (e.g. rescue priority to different vulnerable groups) while maintaining low sampling requirements. Figure provides an overview of method.
To our knowledge, this is the first framework of its kind to explicitly consider both objective and subjective ethical evaluation criteria. A key nuance in this design the interplay of objective metries and stakeholder preferences. Stakeholder preferences are affected by and dictate regions of importance in the objective landscape, and strategy to explore these regions the objective metrics must adapt to stakeholders. This dual dependency is highly non-trivial, and not explicitly acknowledged in prior works. We incorporate this dependency that incorporates feedback from both models for proposing challenging test cases (Seettion 4). We evaluate SEED-SET on three tasks for ethical evaluation: Power system resource allocation, Fire rescue by aerial autonomous agents, and Optimal route design in urban traffic [Secttion 5). Our method successfully scales to high dimensional scenarios, by proposing up to 2x more optimal test cases compared to baselines.
the design our novel data acquisition strategy,
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Our contributions can be summarized as follows:
We introduce a unified, domain-agnostic problem formulation for system-level ethical testing, modeling it an adaptive, sample-constrained inference task over both objective metries and subjective values.
We formalize a hierarchical Variational Gaussian Process (VGP) model that maps design parameters to measurable ethical criteria and learns their utility according subjective factors.
We derive a novel joint acquisition criterion for hierarchical models that balances exploration of uncertain ethical factors with exploitation of learned ethical preferences in our hierarchical VGP.
2 RELATED WORK
We discuss key related works here, with detailed discussion in Appendix A.I
Governance approaches for responsible AI. A wide range of governance frameworks, guidelines and standards have been proposed to guide the ethical development and deployment of Al systems Organisation for Economic Co-operation and Development [0199] [EEE Global Initiative 2019: Winfield et al. 2021 ISO 2024)). (02)2) These works articulate high-level values but are
(Tabassi 2023
vague about specific mechanisms for practical enforcement (Hagendorfi, 2020). For domain-specific implementation of these guidelines, automated ethical evaluation tools have been proposed in the
literature.
Automated tools. Prior technical approaches include reinforcement learning and orchestration for instilling ethical values (Noothigattu et al.) 2019). large-scale studies of moral judgment in LLMs (Zaim bin Ahmad & Takemoto (025), and active learning for preference elicitation (Keswani al. 2024). With the exception of active learning, these techniques impose large-scale data and simulation budget requirements and lack interpretablity and modularity provided by our framework.
PROBLEM STATEMENT
We formulate system-level ethical testing follows:
Problem 3.1. Given a black-box autonomous system Sn. parameterized by policy т E II (such as power resource allocation, drone navigation in environment), evaluate its ethical alignment by querying it in scenarios a E X (environment properties such as location of assets), collecting objective evaluations y y (such cost, resilience), and estimating an unknown ethical compliance function f II x X + IR that captures both objective outcomes and subjective value judgments.
We list some design choices meet the key requirements of our problem formulation:
Multi-faceted ethical criteria. The overall subjective evaluation depends on some task sseciff,, and some task- agg--sgo parameters. For example, stakeholder in power resource allocation will prefer low cost and high grid reliability, regardless of the system specifics, such as grid size. Thus, decompose ethical compliance x)) into two parts: set of objective metrics sobj X ) (c.g., cost, reslience) which can be modeled analytically using prior knowledge (domain experts, guidelines) and user specific subjective evaluation fsubj y cost, resilience as metrics), with limited access to ground-trute evaluations. For a given y. f (v) represents the degree of ethical alignment with the subjective evaluation criteria.
(e.g., perceived fairness using
Sample-constrained learning. Evaluation is costly: querying S, in scenario incurs cost c(T, x)). We approach this using a sequential design paradigm. Given a total testing budget B. the goal of Bayesian Experimental Design is to sequentially select query points to best learn f within budget, to maximize the amount of information obtained about the model parameters of interest. This promotes sample efficiency by utilizing information from collected data D = {x11, vI), (In, yr)).
Scalability and uncertainty modeling. The space II X may be nigh-dimensional, and complex, and evaluations may inherit uncertainty from experts and stakeholders. We model this uncertainty cumulatively in the objective evaluation as y ( ox))., by assuming the noise follows a zero- mean normal distribution with standard deviation cT. Ethical testing thus requires explicit uncertainty modeling (c.g., via Bayesian inference) and scalable function approximation (e.g., variational models), to guide testing toward the most informative scenarios.
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Assumptions. We make the following assumptions about the system under test S,, and the user:
A1 The policy π is fixed during testing, and the scenario space X' is known and fixed priori.
A2 The user provides their true subjective ethical preferences (i.e., users do not misreport), as the ethical evaluation depends the user-defined notion of "good" "preferred" behavior. Additionally, assume that given a set of objectives, the user's latent ethical model is stationary, corresponding to an unknown but fixed subjective criterion.
A3 We additionally assume access to objective evaluations (e.g., damage caused by fire), which are required for subjective evaluation. The complete set of objective metries is assumed to be fully known priori.
Assumption A1 pertains to system requirements, and is a widely used, fundamental assumption in BO/BED literature (Rainforth et al. [0224] Chaloner Verdinelli 1995). This needed to ensure fixed feature space for learning surrogate models for objective and subjective criteria.
Assumption A2 rooted in pairwise elicitation literature that assumes stationary latent utility
function associated to preferential evaluation (Chu & Ghahramani 2005). Note that our framework still accommodates stochasticity evaluation using a probabilistic framework, assuming a stationary latent utility model.
Assumption A3 lists assumptions on objectives, and is also made in prior works investigating composite optimization and preferential evaluation using human feedback (Christiano al. 2017
Lin et al. 2022 Astudillo & Frazier (019)) which consider human feedback over explicitly available "observables generated from system. This also mirrors real-world preference elicitation settings,
where user judgments are anchored to clearly defined objective attributes (Shao et al. 2023
Following the hierarchical distinction of f, meet the multifaceted ethical evaluation requirement using the design of a hierarchical surrogate model, learnt using data queried by our proposed acquisition strategy, in a Variational Bayesian Experimental Design loop. Furthermore, we mitigate the scalability constraints with human evaluation using LLM as proxy evaluators, for a fixed ethical criterion. We now discuss the specifics of our methodology.
SEED-SET
Our approach consists of three main modeling components, hierarchical VGP for surrogate modeling, a data acquisition strategy to generate test cases, combined with an LLM as proxy evaluator for pairwise preferential evaluation. Figure provides an overview of our approach.
4.1
VARIATIONAL BAYESIAN FRAMEWORK FOR SCALABLE ETHICAL MODELING
Our three main components interact with each other with inherent stochasticity from system ob- servations and uncertainty from limited user evaluations. To account for these considerations in sample-efficient evaluation setting, we adopt a variational Bayesian framework. Specifically, we learn surrogate model for using f f(x) (x)()) by applying joint distribution over its behavior at each sample x E X. The prior distribution of the objective p( p((()) is combined with the like- (=)))((()))))
lihood function (x)) which represents the updated beliefs about fxx). We approximate the posterior p(((x))[ using variational distribution parameterized by o, as (f(((z)). for sample-efficien posterior estimation.
compute the posterior distribution p(f p(f())
In this work, we use GPs to estimate the posterior distribution, due to their analytical compatibility with evaluation under limited data. In GP models, the distribution a joint normal distribution p(f (D:) [((()), k(x,,')) _²)) completely specified by its mean μ(rr) and kernel function k(r, x'), where μa(x) represents the prediction and k(æ,, x²) the associated uncertainty. The computational complexity of GP models scales with O as the number of observations n increases. To ensure scalability with the number of observations, we generalize our variational posterior models de to Variational GPs (VGPs) (Tran al. 2015), that reduce the computational burden of inference through sparse approximation of the posterior distribution. detailed discussion on the computational efficiency of VGPs is provided in Appendix A.3
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In this way, the complexity of inference is reduced from O to ( which significantly improves the efficiency if m < n.
Hierarchical VGP (HVGP) for Modular Modelling. Ethical evaluation in autonomous systems is inherently hicrarchical: system designs give rise to observable behaviors measured by fag. which in turn elicit subjective ethical evaluations fuby from user. To capture this structure in scalable and interpretable way, we decompose the ethical evaluation task into two distinct modeling stages, each represented by VGP:
Objective GP, which models the mapping fay using surrogate g + where R are objective metrics, intermediate quantities that reflect system behaviors relevant to ethical concerns (e.g., cost of decision decision-making, resilience, equity in resource distribution across stakeholders, etc.). Subjective GP, which models the mapping fsa using a surrogate h + where IR denotes a latent utility score representing stakeholder judgments (e.g.. perceived fairness or acceptability), obtained from qualitative evaluations.
Subjective ethical evaluation lacks ground-truth values, i... we do not have direct access to label This makes supervised training infeasible. We therefore adopt a common practice for grounding qualitative information involves preference elicitation using pairwise evaluation from an oracle y') + [1,2] (Huang al.] 2025), which are objectives from scenarios (x, x'). Here, the oracle takes a pair of evaluations y E V and returns binary label "1" or "2", indicating its preferred design (41" y y' and vire-)
This hierarchical structure offers two critical advantages: 1) Interpretability: Ethical preferences are grounded observable system outcomes y, not the latent design parameters x. Modeling hyy instead of f(zz) aligns with how stakeholders assess ethical outcomes in terms of behaviors they can perceive and evaluate. 2) Data efficiency: By incorporating the subjective criteria's dependency on a combination of task task-specific and task-agnostic objectives, we promote accurate modeling choices. For accurate evaluation in limited evaluations, sample efficiency and quality of evaluation is highly sensitive modeling choices (Keswani al. 2024
Efficient data querying is critical under limited budgets. Naive random sampling wastes evaluations and often misses key test cases. Moreover, objectives both shape and shaped by subjective evaluations, making separate model training ineffective. Instead, one must target regions of objective space aligned with subjective criteria. We address this through adaptíve data acquisition within Bayesian Experimental Design (BED) framework.
4.2
A BAYESIAN EXPERIMENTAL DESIGN LOOP FOR ADAPTIVE AND EFFICIENT TESTING
Given history of experiments == {(11 v1),..., (In }n)), BED seeks to maximize the Expected Information Gain (EIG) that potential experimental outcome provide about the model parameter of interest, denoted as This is measured as the expected reduction in entropy H(-) of the posterior distribution of 0:
EIG(x) H ()) D) H (p( D (x, g))ll T(0; (),
where, I denotes the mutual information between 0 and (x, y).
We generalize this paradigm into our HVGP models and propose a nested approach that unifies the exploration and exploitation of both the objective factors and subjective preferences simultaneously.
We consider two variational distributions (-(() and qe (+), of the Objective and Subjective GP, parameterized by o, & respectively. We propose V
E:
V(x) (g=i + Eystul=) [ (hyy =|D) + (gg)
(2)
such that two jointly evaluated candidates [x1, can be obtained by maximizing V. Here Jas by denote g(x) and h(y) respectively. Maximizing the first two terms maximizes mutual information in scenario and objective spaces, while the third enforces preferential alignment with proposed criteria.
Equation reflects the inherent hierarchical structure necessary for accurately modeling ethical preferences. The first term captures the expected information gain about the objective layer, ensuring that we reduce uncertainty in the objectives. The second term quantifies information gain in the subjective layer, directly improving our estimate of the latent utility function h(y). The final term
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Figure Environments for the two case studies considered in this work. (Left) Power Grid Allocation IEEE 5-Bus and 30-Bus (Section 5.1 (Right) Fire Rescue (Section 5.2). Additional case study for Optimal Routing in Appendix
encourages sampling in regions where the current model predicts higher ethical utility, enabling the requires
method to balance exploration with targeted exploitation. Balanced all three. In [Section 5] we study acquisition ablations, and in [Section 5.1] stakeholder ablations on the learned objective space. In Anpppedii I we demonstrate the advantage of joint acquisition in sample efficiency and discovery of scenario with high alignment.
4.3 AN LLM-BASED PROXY FOR SUBJECTIVE ETHICAL EVALUATION
The pairwise elicitation oracle is modeled using humans for evaluation. However, using human experts can lead constraints on the number of pairwise evaluations that can be performed. Additionally, getting true experts who are not biased to provide the subjective evaluation can be challenging, especially for understudied domains, and not cost-effective. To reduce the dependency on user evaluation, we leverage LLMs as proxies to make evaluations on behalf of stakeholders according to certain maakhhoddr--peccfeee criteria encoded through prompt design (Huang et al., 2025). Our proposed prompt design accommodates the hierarchical structure proposed so far.
Prompt Design: The prompt has three main parts: 1) Task description: Specifies task-relevant contextual details, 2) Objective metrics (11, W2): For two scenarios (x1, x22). corresponding objective metries (y1, y2) for chosen objectives are provided for comparison, 3) Subjective criteria: An NLP description of preference candidate. These details along with response instruction are used to extract binary preference "1" V1 V2 from the LLM. We conduct ablation studies model and temperature in in[Appendix Fjthat validates the reliability of LLMs as proxy evaluators.
the objective landscape, that encodes criteria for selecting the preferred
5 EXPERIMENTS
Our central hypothesis is that SEED-SET enables scalable, accurate, and data-efficient ethical evalu- ation of autonomous systems. Using previously discussed ethical evaluation concerns (Battistuzzi cet al. 2021 Luo et al., 2024; Bieler et al.) 2024) to guide the design of our observables, we test our hypothesis several case studies across three main applications. In addition, ablations to better understand our methodology. In all plots, solid lines are the mean μ and shaded areas are one standard deviation (p + σ). We run for five seeds per experiment, using GPT-4o for all LLM queries. Additional details are provided in Appendix A.5
conduct several
Benchmarks. We propose three case studies, exploring real world applications using Power Grid Resource allocation, Fire Rescue (as illustrated in Figure 2). and Optimal Routing case study (Appendix E... We also explore multi stakeholder evaluation in Apppendix Gland applicability of our method on real human data in Appendix H| To the best of our knowledge, there are no standard simulation platformss/eenhhmarks to test domain- aggnotic ethical benchmarking techniques for low- budget experimental validation. We discuss more about each case study in the coming sections.
Baselines. We test our SEED-SET framework against the following relevant baselines. First, the Random sampling baseline samples uniformly in the design parameter space. Single GP (Keswani

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5-Bus
30-Bus
Preference Score
7
25
50 Iterations
100
25
50
75
100
Iterations
HVGP
Single GP
Random
VS-AL-1
VS-AL-2
Figure 3: Power Grid Allocation Preference Scores. Preference scores baseline comparison for
5-Bus (left) and 30-Bus (right).
et al. 2024 is pairwise preferential GP that directly maps design parameters consisting of pair of scenarios (z,z') to ethical evaluations 2. VS-AL-1 and VS-AL-2 are Version Space Active Learning baselines, referenced in (Keswani al. 2024 that use Support Vector Machine (SVM) to learn preferential decision boundary for pairs of scenarios (I, x'), with a linear kernel and RBF kernel respectively. Finally, we also compare against BOPE, i.e., Bayesian Optimization with Preference Exploration (Lin et al. (2022) in Appendix which decomposes the two stages of preference exploration and experimentation.
Metries. Since we optimize over the preferences of LLM-proxy stakeholders, we do not actually have access the ground truth preference function. Instead, for each baseline and case study, we handcraft a deterministic preference score function h - representing the ground truth preference over objectives given the observables. The function is designed to be proportional to fuuj- To validate the correctness of the preference score function, we utilize TrueSkill Bayesian skill ranking (Herbrich et al. 2006) to predict scores for each evaluation point. We also use other task specific metrics for evaluating the quality of generated scenarios, such measuring distribution overlap with real data (Appendix H), estimating coverage in feature space to measure the degree of exploration (Section 5.2), and qualitative visualizations (Section 5.1).
5.1 POWER GRID RESOURCE ALLOCATION
We first study the ethical impact of using different Distributed Energy Resource (DER) deployment strategies with varying reactive power limits on the Power Grid Allocation IEEE 5-bus and IEEE 30-bus networks (Luo et al. 2024), denoted 5-Bus and 30-Bus for convenience.
Scenario Description. A scenario is parameterized by [1 E X, where E f0, 1 is binary vector indicating a certain location has DER deployment, and r E IR? specifies the reactive power limits. This is challenging problem for ethical evaluation due multifaceted ethical concerns in distribution of resource arising from various stakeholders (Luo et al. 2024 Bieler et al.) 2024).
Observables. Given scenario æ, the resulting observables vector y R has four components. The voltage Fairness (y') measures the uniformity of voltage distribution across all buses. The total Cost (y²) combines the expenses of installing DER units and reactive power provision. The Priority (y²) area coverage measures how well each design serves under-served or rural buses. Finally, the Resilience (w*) assesses the network's ability to maintain voltages above specified threshold. We provide more details, including formulas, in Appendix B.0.3
Evaluation Method. In our prompt, ask the LLM to prioritize Priority, followed by Cost, and ignore all other dimensions (prompt example in Appeendix B.O.7). Since we do not have access to ground truth evaluation scores, we approximate the LLM's preference function with preference
score function h(y)) = [0, 10.50.5,1 0ly.
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Priority Stakeholder
Cost Stakeholder
Iteration (Normalized)
1.0
24
24
1.0 6 meen
anne (Normalized]
Priority
0.5
0.5
0.0
0.0
35
0
35
Cost
Cost
Figure Bus-30 Different Stakeholder Groups. We show that our learned preference GP is able to adapt to the needs of different potential stakeholder groups. The plots show data point for optimum preference score (shown in red) projected on objective space, with contours of predicted preference score for Stakeholder A (left) and B (right), with optimum value data point.
Results We evaluate on 5-Bus and 30-Bus and observe that our proposed HVGP achieves higher preference score than all other baselines. Although Single GP can do better than Random on 5-Bus, it cannot solve 30-Bus since the design parameter dimensions grows to 40, making hard to efficiently explore the space. On the contrary, HVGP can mitigate this through the hierarchical structure, which reduces the complexity of mapping from objectives subjective assessment, followed by our acquisition strategy, which prioritizes targeted exploration of objective space through the second mutual information term (MI2).
Both VS-AL-1 and VS-AL-2 cannot solve either tasks, which we hypothesize is because of the inaccurate modeling choice in VS-AL-1 due learning a linear decision boundary. Similar reasoning as Single GP can be used to explain the inefficiency of VS-AL-2 due to its direct modeling of complex decision space.
5.2 FIRE RESCUE
Next, we consider an autonomous drone navigation scenario for fire extinguishing in a cami-urban setting, motivated by discussions ethical concerns in rescue roboties (Battistuzzi et al. 2021.
Scenario description. A Fire Rescue scenario is parameterized by [0, 1| with only 9 dimensions relevant to scenario design, controlling the placement of assets such as a museum, a gas station, food court, and residential blocks with tree covers of variable density. The remaining dimensions are uncorrelated to the objectives. In FFgure of Appendix C), we give examples of three scenario visualizations. The goal of the autonomous system to search the area for fire and, based on its uncertainty, decide whether to continue exploring or spray retardant.
Observables. Given scenario I, the observables vector y = ly' 12 E R quantifies the cumulative potential Chemical Damage caused by deciding to spraying the retardant ( cumulative potential Fire Damage caused by fire due to deciding not to spray the retardant (z²), and Spread fuctor(y"), measuring risk of firespread due to proximity of assets.
Evaluation Method. In our prompt, we ask the LLM to prioritize high Chemical damage and high
Spread factor (prompt example Appendix C). We use preference score function h((y) = [1, 1ly and coverage score function č(x) that estimates cumulative standard deviation for [. for n collected data points to measure the coverage of search space to sample novel scenarios.
Results. We observe that HVGP achieves higher preference score than all baselines, with similar explanation as provided in the results of Section 5.1 We also observe that our acquisition strategy achieves higher preference score than two HVGP variants: MI1+MI2, and Pref. MI1+MIB does not consider the preference term in the acquisition function, and Pref does not consider the two mutual information terms. The discrepancy in the preference scores is due to MI1+M12's inefficient
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Preference Score
Preference score
HVGP
VS-AL-1
Random
Iterations
Single
VS-AL-2
HVGP
MI1 M11+MI2
Pref
Figure 5: Fire Rescue Preference Scores. We report preferences scores for baseline comparisons (left) and acquisition strategy ablations (right).
Coverage score
0.12
0.30
9.30
Coverage
0.08
Iterationn VS AL-2
23
60
63
130
lterations
Single GP
Figure 6: Fire Rescue Coverage, We report coverage scores for baseline comparisons (left) and acquisition strategy ablations (right).
exploration in higher dimensions. While Pref performs better than MI1+ the complete acquisition strategy performs the best with the additional improvement from targeted exploration. Our method also shows higher coverage scores than baselines and ablations. We also provide an example of scenario generation using the learned model (Appendix C.O.4).
5.3 ADDITIONAL RESULTS
To better understanding how SEED-SET works, we perform several ablation studies in a question- and-answer format.
(Q1) What conditions enable SEED-SET to perform well? We observe that our method performs best when the design parameter space is large. In lower-dimensional settings such 5-Bus. Single GP performs well but still suboptimal compared to our method (Figure (left)). However, in nigher-dimensional cases such as 30-Bus and Fire Rescue, HVGP outperforms Single GP by exploring test cases that highly align with the subjective criteria. We also observe superior performance of our method compared to ablations of BOPE in Ampppeddxx I which shows that in limited sample settings, joint learning of objective and subjective models improves sample efficiency.
(Q2) Do our handcrafted preference score functions well approximate the LLM's preference function? It does. In Figure 7| we treat each sampled observable as player in free-for-all game, and use the TrueSkill Bayesian skill rating system (Herbrich et al.) 2006) to
5-Bus
30-Bus
45
15
evaluate their individual skill ratings (evaluation details in
Appendix B.O.2). This can be more accurate evaluation method than our proposed preference scores since does not assume the LLM's optimization objective form. How- ever, this evaluation process can be extremely expensive and the skill ratings seeds and baselines. We observe that for both 5-Bus and 30-Bus, the trends roughly match the trends seen from the heuristic preference scores in Figure 3
HAGP
Single
Random
VS-R2-
VS-Au2
Figure 7: Power Grid Allocation Skill Ratings. Skill ratings computed using a Bayesian skill rating algorithm (Her brich et al. (2066).
not directly comparable across

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(Q3) How does acquisition strategy support sample efficiency? In the acquisition ablation studies, we observe that our complete acquisition strategy consistently performs well. In Fire Rescue (right of Figure 5). while preferential optimization is crucial, mutual information enables efficient exploration, which is essential a high-dimensional search space with low volume optima, leading to incremental improvement over Pref. This validates idea of balancing exploration and exploitation with the acquisition strategy.
(Q4) How well does our model adapt to different stakeholders? In power resource allocation, we consider an ablation with two stakeholders. Stakcholder A and care mainly about high priority, and low cost respectively. Figure 4sshow the optimum value scenario for Stakeholder A has a high priority score, and high cost, whereas for Stakeholder B, the optimum corresponds to low cost and low priority. This shows that the sampling procedure accurately accounts for the stakcholder-specifie criteria, resulting in different explorations, and stakcholder-speciffe test cases, We also extend this analysis to more challenging, multi-stakeholder setting for the Power Grid case study (details in Appendix G), Figure 16 and observe similar trends.
(Q5) How robust is our approach to LLM specifications? We conduct ablation studies for the Fire Rescue case study for LLM parameters, varying temperature, prompt and model (Appendix F). and observe robustness to evaluator perturbations. We attribute this to pairwise elicitation, that climinates uncertainty from celf-inconsistency (Bouwer et
2024 Hoeijmakers al. 2024).
(Q6) How suitable our framework for real-world ethical alignment? In Appendixx H, we apply our framework to extract latent objectives influencing travel mode choices using the TravelMode dataset (Greene 2003). This case study shows that our framework can be effectively applied to learn underlying trends in objective landscape (Figure 17).
LIMITATIONS
While the SEED-SET framework effectively mitigates common challenges associated with ethical assessment, certain limitations remain, along with promising future directions.
Scalability for Extremely Large Datasets. Using sparse variational GPs reduces complexity from O(N") to (?) with M inducing points, enabling SEED-SET to handle tens of thousands of observations. Scaling to hundreds of thousands or more remains challenging, which future work could address via stochastic variational inference (SVI).
The current model uses a stationary kernel (e.g., RBF), assuming covariance depends only on relative distance. This be too restrictive for systems with varying regimes. To relax this, SEED-SET can be extended with non-stationary kernels (e.g.. spectral mixture, warpt- or deep GPs that warp inputs through neural layers. Our model requires complete knowledge of objective metrics priori. While this is a reasonable assumption that mimics real-world preference elicitation, in the lack of complete list of objectives, the testing process can have inaccuracies.
Using LLMs as ethical proxies also risks sensitivity to prompts and context, so ongoing alignment checks fine-tuning are needed keep their judgments sync with human values. Still, when no ground truth exists, preference data is practical surrogate, and VGPs with Bayesian design offer sample-efficient solution that would be far cheaper than training an LLM from scratch on preferences.
CONCLUSION
We presented SEED-SET. a scalable framework for ethical evaluation of autonomous systems that combines objective system metrics with subjective stakeholder judgments through a hierarchical variational Bayesian model. By separating measurable factors from user preferences and guiding exploration via principled acquisition strategy, SEED-SET enables efficient and interpretable evaluation of ethical trade-offs. The integration of large language models as proxy evaluators further reduces human burden while maintaining value alignment. Experiments across domains demonstrate SEED-SET's effectiveness, with future work aimed at extending to multi-agent settings and real-time applications.
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8 ETHICS STATEMENT
In this work, we propose an automated evaluation tool for ethical assessment of autonomous systems. Our work assumes provided ethical criteria for evaluation, and associated cost functions. We make no recommendations or comments on the correctness of any ethical criteria in this work, and mainly leverage existing instances of ethical evaluation for validation of our pipeline. The paper does not involve crowdsourcing or research with human subjects.
9 REPRODUCIBILITY STATEMENT
All simulations for Fire rescue, Power resource grid allocation and Optimal Routing were conducted on Linux workstation with Ubuntu 22.04 LTS equipped with Intel 13th Gen Core i7- 17-13700KF CPU (16 cores, 24 threads, to 5.4 GHz) and an NVIDIA GeForce RT' 4090 GPU (24 GB ARAM).
The Bayesian Experimental Design (BED) loops were implemented using wrapper BoTorch (Balandat al. (020) and GPyTorch (Gardner et al. 2018) libraries. The compute requirements were consistent with standard usage of these libraries and did not require additional specialized hardware beyond what was used for Webots simulation. Implementation specific details of both the simulations provided in in/Appendix Bjand[Appendix CJ Examples of LLM prompts used in the generation of results reported in the paper are also provided in the Appendix.
10 ACKNOWLEDGEMENT
This research developed with funding from the Defense Advanced Research Projects Agency (DARPA) with Distribution Statement "A" (Approved for Public Release, Distribution Unlimited). The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of DARPA or the U.S. Govermment.
We thank Jonathan Becker, Ashley Wiren, and Mitul Saha for their valuable discussions and support throughout the project.

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REFERENCES
Trueskill: the video game rating system. 2ttps://EruesklL/
Iso/pas-8800 road vehicles safety and artificial intelligence, 2024.
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. Concrete problems in ai safety. arXiv preprint (tte::666006665. 2016.
Raul Astudillo and Peter Frazier. Bayesian optimization of composite functions. In International Conference Machine Learning, pp. 354-363. PMLR, 2019.
Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G Wil- 8on, and Eytan Bakshy. Botorch: A framework for efficient monte-carlo bayesian optimization. Advances in neural information processing systems, 33:21524-21533, 2020.
Linda Battistuzzi, Carmine Tommaso Recchiuto, and Antonio Sgorbissa. Ethical concerns in rescue robotics: scoping review. Ethics and Information Technology, 23(4):863- 875, 2021.
Sanjukta Bhattacharya, Anastasios Oulis Rousis, and Aikaterini Bourazeri. Trust & fair resource allocation in community energy systems. IEEE Access, 12:11157-1119,, 2024.
Stephanie Bieler, Cara Goldenberg, Avery McEvoy, Katerina Stephan, and Alex Walmsley. Aggregated distributed energy resources 2024: The fundamentals. Technical report. RMI, under contract with NARUC, July 2024. URL ps: /coommctedcooonnnntitaeeee wew.eeeee....oo..............l.. Ltterenttta___ttttttiiiiiie Interactive. Prepared as part of the NARUC-NASEO DER Integration and Compensation Initiative, DOE Award No. DE-00F0000255.
Sites/default/EEr
/00ps//2244000000 NARUC ADER Fundamentals
pdf
Abeba Birhane, Sepehr Dehdashtian, Vinay Prabhu, and Vishnu Boddeti. The dark side of dataset
scaling: Evaluating racial classification multimodal models. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 1229-1244, 2024.
Renske Bouwer, Marije Lesterhuis, Fien De Smedt, Hilde Van Keer, and Sven De Maeyer. Compar- ative approaches to the assessment writing: Reliability and validity of benchmark rating and comparative judgement. Journal of Writing Research, 15(3):498-518, 2024.
Kathryn Chaloner and Isabella Verdinelli. Bayesian experimental design: review. Statistical science, pp. 273-304, 1995.
Gregory Chance, Dhaminda Abeywickrama, Beckett LeClair, Owen KeIr, and Kerstin Eder. Assessing trustworthiness of autonomous systems. arXiv preprint arXiv:2305.03411 2023.
Hareesh Chitikena, Filippo Sanfilippo, and Shugen Ma. Roboties search and rescue (sar) operations: An ethical and design perspective framework for response phase. Applied Sciences, 13(3):1800, 2023.
Paul Francis Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. ArXiv, abs/1706.03741, 2017. URI.
ht https: www.nt
ottg Cttg:/Crrpssl....m 4787508
Wei Chu and Zoubin Ghahramani. Preference learning with Gaussian processes. In Proceedings of the 22nd international conference on Machine learning, pp. 137-144, 2005.
Shiyang Cong, Destenie Nock. Yuchen L. Qiu, and Bo Xing. Unveiling hidden energy poverty using the energy equity gap. Nature Communications, 13(1):2456, 2022. doi 10.1038//41467-022
30146-5
Louise Dennis, Michael Fisher, Marija Slavkovik, and Matt Webster. Formal verification of ethical choices in autonomous systems. Roboties and Autonomous Systems, 77:1-14, 2016.
Andrés Domínguez Hernández and Vassilis Galanos. A toolkit of dilemmas: Beyond debiasing and fairness formulas for responsible ai/ml. arXiv preprint arXiv=2303.01938) 2023.
12

---

Published as a conference paper at ICLR 2026
Ahmed Fahmin, Muhammad Aamir Cheema, Mohammed Eunus Ali, Adel Nadjaran Toosi, Hua Lu, Huan Li, David Taniar, Hesham A. Rakha, and Bojie Shen. Eco-friendly route planning algorithmss Taxonomies, literature review and future directions. ACM Computing Surveys, 57(1):1-42 2024.
Xin Gao, Tian Luan, Xueyuan Li, Qi Liu, Zhaoyang Ma, Xiaoqiang Meng, and Zirui Li. Ethical alignment decision-making for connected autonomous vehicle in traffic dilemmas via reinforcement learning from human feedback. IEEE Internet of Things Journal, 2024.
Jacob Gardner, Geoff Pleiss, Kilian Q Weinberger, David Bindel, and Andrew G Wilson. Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration. Advances in neural information processing systems, 31, 2018.
Declan Grabb, Max Lamparth, and Nina Vasan. Risks from language models for automated mental healthcare: Ethics and structure for implementation. arXiv preprint arXiv:2406. 1852, 2024.
William H. Greene. Econometric Analysis. Prentice Hall, Upper Saddle River, NJ, edition, 2003.
Thilo Hagendorff. The ethics of ai ethics: An evaluation of guidelines. Minds and machines, 30(1):
99-120, 2020.
Ralf Herbrich, Tom Minka, and Thore Graepel. TrueskillTM; a bayesian skill rating system. In Proceedings of the 20th International Conference Neural Information Processing Systems, NIPS 06, pp. 569-576, Cambridge, MA, USA, 2006. MIT Press.
Eva Hoeijmakers, Bibi Martens, Babs MF Hendriks, Casper Mihl, Razvan L Miclea, Walter H Backes, Joachim E Wildberger, Frank M Zijta, Hester A Gietema, Patricia J Nelemans, et al. How subjective ct image quality assessment becomes surprisingly reliable: pairwise comparisons instead of likert scale. European Radiology, 34(7):449444500 2024.
David Huang, Francisco Marmolejo-Cossfo, Edwin Lock, and David Parkes. Accelerated preference elicitation with llm-based proxies, 2025. URL https:/:::
/tattpp://arxxv.ooo arwaa.iiiivvooo wttpxxiv.or//ab//500000 14625
IEEE Global Initiative. Ethically aligned design: A vision for prioritizing human well- being with autonomous and intelligent systems.
gloobl-inne Aelobal-initinn htt.:/acieeeetooooooo/o////////a 2019.
Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni, Maarten Sap, Regina Rini, and Yejin Choi. Can machines Iearn morality? the delphi experiment. arXiv preprint arXiv: 10.07574, 2021.
Anna Jobin, Marcello Ienca, and Effy Vayena. The global landscape of ai ethics guidelines. Nature machine intelligence, 1(9):389-399 -998, 2019.
Vijay Keswani, Vincent Conitzer, Hoda Heidari, Jana Schaich Borg, and Walter Sinnott-Armstrong. On the pros and cons of active learning for moral preference elicitation. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 7, pp. 711-723, 2024.
Zhiyuan Jerry Lin, Raul Astudillo, Peter Frazier, and Eytan Bakshy. Preference exploration for efficient bayesian optimization with multiple outcomes. International Conference on Artificial Intelligence and Statistics, pp. 4235-4258. PMLR, 2022.
Yinhong Liu, Han Zhou, Zhijiang Guo, Ehsan Shareghi, Ivan Vulić. Anna Korhonen, and Nigel Collier. Aligning with human judgement: The role of pairwise preference in large language model evaluators. arXiv preprint arXiv:2403. 16950, 2024.
Dongwen Luo, Jiachen Zhong, Yiting Wang, and Weihao Pan. Ethical considerations in smart grid optimization promoting energy equity and faimesss 2024.
Karel Martens. Transport justice: Designing fair transportation systems. Routledge, 2016.
Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, et al. Artificial intelligence index report 2025. arXiv preprint 073 2025.
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Published as a conference paper at ICLR 2026
Ninarch Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. arXiv preprint 1rXiv:190..09633 2021.
Olivier Michel. Cyberboties Itd. webots professional mobile robot simulation. International
Journal of Advanced Robotic Systems, 1(1):5, 2004.
Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi. The ethics of algorithms: Mapping the debate. Big Data & Society, (2))20539517716679966 2016.
Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush R Varshney, Murray Campbell, Moninder Singh, and Francesca Rossi. Teaching ai agents ethical values using reinforcement learning and policy orchestration. IBM Journal of Research and Development, 63(4/5):2-1, 2019.
Organisation for Economic Co-operation and Development. Oced principles on artificial in- telligence. https://wwet oecd. html 2019.
Przemyslaw Palka. Ai, consumers psychological harm. Al and Consumers,Lann DiMatteo, Cristina Poncibò, Martin Hogg, Geraint Howells (Eds.), Cambridge University Press (2023/2024), 2023.
Rafael HM Pereira, Tim Schwanen, and David Banister. Distributive justice and equity in transporta- tion. Transport reviews, 37(2):170-19) 2017.
Tom Rainforth, Adam Foster, Desi R Ivanova, and Freddie Bickford Smith. Modern bayesian experimental design. Statistical Science, (011):00-114 2024.
Anka Reuel, Ben Bucknall, Stephen Casper, Tim Fist, Lisa Soder, Onni Aame, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, et al. Open problems in technical ai governance. arXiv preprint 2rXiv:2407.14988 2024.
Ann-Katrin Reuel, Mark Koren, Anthony Corso, and Mykel J Kochenderfer. Using adaptive stress testing identify paths to ethical dilemmas in autonomous systems. In SafeAI@ AAAI, 2022.
Olawale Salaudeen, Anka Reuel, Ahmed Ahmed, Suhana Bedi, Zachary Robertson, Sudharsan Sundar, Ben Domingue, Angelina Wang, and Sanmi Koyejo. Measurement meaning: A validity-centered framework for ai evaluation. arXiv preprint arXiv:2505.1057: 2025.
Murat Sensoy, Lance M. Kaplan, Simon Julier, Maryam Saleki, and Federico Cerutti. Risk-aware classification via uncertainty quantification. arXiv preprint arXiv:2412.0339) 2024.
Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, and Matthew Walter. Eliciting user preferences for personalized multi-objective decision making through comparative feedback. Advances in Neural Information Processing Systems, 36: 2192-12221, 2023.
Benjamin K Sovacool, Raphael J Heffron, Darren McCauley, and Andreas Goldthau. Energy decisions reframed as justice and ethical concerns. Nature Energy, 155):166, 2016.
Elham Tabassi. Artificial intelligence risk management framework (ai rmf 1.0). htttts::/tssppppps nist. gww//pbblllloo cat on get_gee______ 2ttpfffffmmmppuiddd3332222222 January 2023. National In- stitute of Standards and Technology, Gaithersburg, MD. d0:110..028/NSTT.AA.0000
Christian Tarsney, Teruji Thomas, and William MacAskill. Moral decision decision-making under uncertainty. 2024.
Michalis Titsias. Variational learning of inducing variables in sparse Gaussian processes. In Artificial intelligence and statistics, pp. 567-574, 2009.
Dustin Tran, Rajesh Ranganath, and David M Blei. The variational gaussian process. arXiv preprint arXiv: 151 1. (6699, 2015.
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Hanna Wallach, Meera Desai, A Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alexandra Chouldechova, Emily Corvi, P Alex Dow, et al. Position: Evaluating generative ai systems is social science measurement challenge. arXiv preprint arXiv:2502.00561, 2025.
Angelina Wang, Xuechunzi Bai, Solon Barocas, and Su Lin Blodgett. Measuring machine learning harms from stereotypes requires understanding who is harmed by which errors in what ways. In Proceedings of the 2025 ACM Conference on Fairness, Accountabilityy and Transparency, pp. 746-762, 2025.
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359, 2021.
Laura Weidinger, Maribeth Rauh, Nahema Marchal, Arianna Manzini, Lisa Anne Hendricks, Juan Mateos-Garcia, Stevie Bergman, Jackie Kay, Conor Griffin, Ben Bariach, al. Sociotechnical safety evaluation of generative ai systems. arXiv preprint arXiv:2310.06682 2023.
A. F. T. Winfield, S. Booth, L. A. Dennis, T. Egawa, H. Hastie, N. Jacobs, R. I. Muttram, J. I. Olszewska, F. Rajabiyazdi, A. Theodorou, M. Underwood, R. H. Wortham, and E. Watson. leee p7001: A proposed standard on transparency. Frontiers in robotics and AI, 8:665729, 2021.
Peipei Xu, Wenjie Ruan, and Xiaowei Huang. Towards the quantification of safety risks in deep neural networks. arXiv preprint arXiv:2009.06/ 14, 2020.
Muhammad Shahrul Zaim bin Ahmad and Kazuhiro Takemoto. Large-scale moral machine experi- ment on large language models. PloS one, 20(5):e0327776, 2025.
Yi Zeng, Yu Yang, Andy Zhou, Jeffrey Ziwei Tan, Yuheng Tu, Yifan Mai, Kevin Klyman, Minzhou Pan, Ruoxi Jia, Dawn Song, et al. Air-bench 2024: A safety benchmark based on risk categories from regulations and policies. arXiv preprint arXiv:2407.17436. 2024.
Daniel Ziegler. Long Ouyang, Jeffrey Wu, Ryan Lowe, Nisan Stiennon, John Gray, John Schulman, and Paul Christiano. Self-rewarding language models, arXiv preprint 155 2022.
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A LITERATURE REVIEW
A.1 ADDITIONAL RELATED WORKS
Governance Approaches, for Responsible AL A wide range of governance frameworks, guidelines and standards have been proposed to guide the ethical development and deployment of Al systems
(Tabassi
2023
Organisation for Economic Co-operation and Development 2019 TEEE Global
Initiative 2019). For example, NIST's AT Risk Management Framework (AT RMF 1.0, 2023),
TEEE's P7001 transparency levels (Winfield et al. [022] or ISO PAS 8800 on ethical design (ISO) 2024b). Hagendorff's s meta-analysis (Hagendorti) 2020) highlights the vagueness and redundancy in many such documents. Recent reports and position papers also emphasize broader societal impacts, including the Al Index 2025 report (Maslej et al., 2025
new perspectives on ML harms and domain-
specific risks (Wang et al.
2025 Grabb et al. 2024 Salaudeen et al., 2025 Reuel et al., 2024]
Wallach et al. 2025 Palka 2023). These works highlight that evaluating AT systems is not only
technical task but also broader measurement challenge.
Recent reports and position papers also emphasize broader societal impacts, including the Al Index 2025 report (Maslej et al. 2025), new perspectives on ML harms and lomain-specific risks (Wang) et al. 2025 Grabb al. )024), and ongoing debates on measurement validity, governance challenges, and consumer harms (Salaudeen et al., 20255] Reuel et al., 2024 Wallach et al., 2025 Palka (023). These works highlight that evaluating Al systems is not only technical task but also broader measurement challenge.
ML-Based Ethical Evaluation. Complementary to governance, researchers have explored ML-based methods for quantifying ethical properties of AI behavior. Fairness metrics such as demographic parity and equalized odds are widely studied (Mehrabi et al., 2021), though their applicability varies across contexts. Risk estimation techniques, including uncertainty-awaat classification models, have been used quantify prediction confidence and manage potential harm in safety-critical domains (Xu et al. 2020 Sensoy et al. 2024). Recent work has also highlighted the need for frameworks that support situated ethical reasoning, emphasizing context, trade-offs, and reflexivity in the design of responsible AI/MI. systems (Domínguez Hernández. & Galanos| 2023). ) while frameworks like Weidinger et al.'s sociotechnical safety evaluation introduce layered approaches that include systemic impacts (Weidinger et al., 2023). More recently, I.LMs have been used as ethical evaluators: some systems learn to score the moral acceptability of generated content (Jiang et al., 202I), while others self-evaluate outputs against behavioral objectives (Ziegler et al. 2022). This motivates the need for practical, system-level methods that can evaluate ethical behavior empirically and at scale. While frameworks and metrics exist in isolation, few approaches integrate them into unified methodology suitable for continuous testing or deployment, which is addressed by our work.
Automated tools. Prior technical approaches include reinforcement learning and orchestration for instilling ethical values (Noothigattu et al., (019)). large-scale moral judgment studies on LLMs (Zaim bin Ahmad & Takemoto 2025), and active learning for preference elicitation (Keswani ct al. 2024). With the exception of active learning, these techniques impose large-scale data and sim- ulation budget requirements. Our baseline models are adopted from active learning based techniques. In addition, ethical concerns have been explored in domain applications such as smart grids (Luo) robotics (Battistuzzi al. 202T). which we leverage for the case studies considered in our work.
al. 2024) and
Bayesian Optimization (BO)/ayeeiian Experimental Design (BED) for sample efficient eval- uation. Works such as Lin et al.) (2022); AAtudilllo & Frazier (219) have explored hierarchical decision making in sample efficient settings within BO/BED paradigm, which we also leverage as the mathematical foundation of our technique. While Lin et al. (2022) addresses joint objec- tive- subjectivee evaluation, its performance depends heavily on careful tuning of design parameters. Our key technical contribution is joint learning of objective metrics and subjective preferences, paired with an acquisition function that integrates both sources of information. This yields significantly improved sample efficiency, making the approach practical for real-world evaluation.
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A.2 VARIATIONAL BAYESIAN METHODS:OVERVIEEW
In variational inference, the posterior distribution over a set of unobserved variables {u,.... "nn given some data 'D is approximated by a so-called variational distribution g(u): p(u(D) =q().
Variational Bayesian methods are a family of techniques for efficient posterior approximation in Bayesian inference. In variational inference. the posterior distribution over a set of unobserved variables u = {ui,-.. ... 2n given some data ' is approximated by so-called variational distribution qu)): p(u D) q()). The distribution (u)) is restricted belong to family of distributions of simpler form than (D) (e.g. family of Gaussian distributions), selected with the intention to minimize the Kullback-Leible (KL.) divergence between the approximated variational distribution q(u) and the exact posterior p(u(D). This is equivalent to maximizing the evidence lower bound
(ELBO) Titssas 2009):
(L)O( Bg(.)) [logr(D)u)) D (kllg()))lp()))
which can be considered sum of the expected log-likelihood of the data and the KL divergence between the variational distribution and the prior p(u) (Titsias. 2009).
A.3 VGPs
In GP models, the distribution is a joint normal distribution p([())))) (u((z), k(,, x²)) com- pletely specified by its mean p=z) and kernel function k(zxx') with corresponding hyper-paramete parameters where p(z) represents the prediction and (xx,, x²) the associated uncertainty. The computational complexity of GP models scales with (nn²) as the number of observations n increases. Sparse Variational GP (SVGP) reduces the computational burden of inference through sparse approximations of the posterior distributions by introducing auxiliary latent variables and Z, where the induc- ing variables u = (u(2))..... u((m)) ]] E RT are the latent function values corresponding to the inducing input locations contained in the matrix Z [21.-.. 2m T Rmdd
Typically, the variational distribution go (u) is parameterized as Gaussian with variational mean my and covariance Su. Assuming that the latent function values f(z), f(z') are conditionally independent given u and x, x' &
J. the GP posterior can be cheaply approximated as
x (o(())) ((o()))l))o(u)dd
= -N(us()),
where
(x(x) = (r))m
as x') kolx, x²)
vT (K Su)Vu(z'),
Vin Ku,
In this way, the complexity of inference is reduced from O(n²) O(mm²,, which significantly improves the efficiency if m & n.
A.4 PAIRWISE BAYESIAN OPTIMIZATION
Following (Chu & Ghahramani 2005), we assume that the responses are distributed according to a probit likelihood used in the construction of V(x) as in equation 2
L( v2) = 1 (g(()) ) g(22) Φ g(v)) - g(12)
/2A
where À is a hyyer-parametee that can be estimated along with the other hyper-parametert of the model, and & is the standard normal CDF We extend this concept to LLM as evaluators, with the same assumptions on probit likelihood modeling.
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A.5 BASELINES AND METRICS
We ablate our problem formulation to the Single GP baseline, which is also popularly used in literature for pairwise preferential elicitation.
Version space active learning (VS-AL) has been adopted from Keswani et al. (2024), and corresponds learning an accurate decision boundary for a pair of scenarios as inputs. VS-AL-1 samples next data-point based on margin maximization from decision boundary using a Support Vector Machine. VS-AL-2 requires an explicit utility function to be supplied, and performs weighted exploration- optimization this utility function controlled by a hyperparameter X. In our experiments, we set the utility function to be the same as preference score. We see that = 0 gives fast and efficient optimization. The results reported in main paper correspond to X = 0.3.
We note that VS-AL does not perform well in our tasks due to the fact that it is designed for prediction accиracy, whereas our method is geared towards online test case generation. This also shows the crucial role of modeling choices in limited data settings, where VS-AL-1 fails due to linear decision boundary assumptions, which do not capture the complex landscape. The sensitivity of VS-AL-2 to noise also renders it unsuitable for noisy explorations such as those we focus on.
B DISTRIBUTED ENERGY RESOURCE ALLOCATION IN POWER GRIDS
We evaluate our distributed energy resource allocation problem on real-world decision-making task modeled after an Optimal Power Flow (OPF) scenario in power systems. The objective is to identify deployment strategies for Distributed Energy Resources (DERs) that align with implicit ethical preferences across multiple performance dimensions.
B.0.1 SYSTEM SETUP
The testbed is the standard IEEE 5/30-bus network, a widely used benchmark in power system studies. We consider variety DER placement and sizing configurations, each representing a distinct design candidate. For each configuration, an AC OPF is solved using the pandapower library to compute physical network states under steady-state conditions. All experiments were conducted using the same computational resources described in Appendix C.0.2
B.0.2 RANKING FOR BAYESIAN INFERENCE
We evaluate the alignment of our proposed method's queried candidates with the LLM evaluator using the TrueSkill Bayesian rating system (Herbrich et al.) (2066) and implemented using [ru] The intuition is that training progresses, the proposed candidates should achieve higher alignment with the LLM's preferences. Thus, later candidates should inherently achieve a higher preference rating than candidates queried earlier during training.
Informally, let X' == {x ........IN NNN } C Rd be the sequence of queried candidate points over the course of single training session. Define the latent utility function u. I E for the LLM evaluator: Since candidates should optimize on the LLM's preference over time, we expect an approximately monotonic improvement in alignment
u(x;)) S u((4+1) <
(3)
Consider each latent utility as a random variable
u(x:) N of)
(4)
We perform Bayesian skill rating inference Herbrich et al. (2006) to obtain the posterior distributions over each (4; and sorting them to obtain the final rankings.
In practice, for each baseline and seed, first downsample the total number of candidate points, down to N points. We define each of the N points as a player in free-for-all game (as mentioned in truj. Then, we take Comb N, combinations of 1-versus-1 games approximate the skill rating
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(or (4) of each player, which is what plot in Figure 7
Each 1-versus-1 game is resolved by treating the game as pairwise comparison standard to our SEED-SEL methodology. where we have an LLM-proxy select which player has higher preference alignment with the LLM. This choice then corresponds to the winning player in the game.
B.0.3 PERFORMANCE METRICS
Each design is evaluated using four ethically motivated metrics:
Voltage Fairness: Measures the variance in bus voltages across the network; lower variance implies more equitable voltage delivery.
Total Cost: Combines capital expenditures for DER installation and operational costs related to reactive power support.
Priority Area Coverage: Quantifies the share of power delivered to high-priority buses, such as rural or underserved regions.
Resilience: Assesses the percentage of time that all bus voltages remain within safe operating limits under perturbations (e.g., load uncertainty or line outages).
B.0.4 PREFERENCE MODELING
Instead of assuming explicit utility weights the objectives, we simulate human-in-the-loop or policy-driven decision-making via pairwise preference queries. That is, for selected pairs of outcomes (V1, V2), preference function indicates which design is ethically preferred. These preferences are generated based on a latent utility function, not revealed to the optimizer, that reflects nonlincar trade-off's among the four objectives.
B.0.5 OPTIMIZATION TASK
The goal is to efficiently identify high-utiligy DER configurations by querying preferences, without direct access to the utility values. This falls under composite Bayesian optimization with preference exploration, where the acquisition function balances exploration of uncertain regions in ethics space with exploitation of inferred preferences.
B.0.6 QUERY STRATEGY
An initial set of 10 pairwise preferences is randomly sampled to initialize the model. Each step of the optimization selects new pairs query. guided by the used acquisition strategy.
B.0.7 EVALUATION CRITERIA
Criterion. Priority primary, Cost secondary (with threshold): Your evaluation should consider both Priority and Cost, with Priority given greater importance. Specifically, you should first compare the scenarios based on their Priority scores. the difference Priority between the two scenarios within small threshold of 0.5 (indicating that their Priority performance is very close), then you should prefer the scenario with the lower Cost, even its Priority is slightly lower. The objectives Faimess and Resilience design for the Power Grid Resource Allocation experiment, as shown Figure The other prompts be created by modifying various user-specified criteria.
not considered in this evaluation. Below, provide the detailed prompt
C FIRE RESCUE
We used an open-source source simulator Webots simulator Michel (0044) for scenario generation and drone navigation using PID controller. We choose to incorporate Chemical Damage observable in response to the ethical criteria of not adding additional risk in rescue robotics tasks suggested in (Battistuzzi et al. 2021).
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*Takk: Your task is to compare and evaluate two optimal power flow (OPF) scenarios a standard IEEE 30-bus network. Each scenario involves installing Distributed Energy Resources (DERs) specified buses. The performance of each scenario is quantified by four key objectives: Voltage Fairness, Total Cost, Priority Area Coverage, and Resilience. Given two scenarios and a user-defined criterion specifying which objectives matter the user and their relative importance, your task is to determine which scenario is preferable according to the criterion.
*Scenario parameters: Each scenario is quantified by outcome vector consisting of four dimensions, representing distinct performance measures: Dimension 1: Fairness (higher is better) Measures the uniformi ty of the voltage across buses, with values closer to indicating more uniform voltages.
Dimension 2: Cost (lower is better) - Represents the total combined cost associated with DER installations and reactive power provision.
Dimension 3: Priority (higher is better) Indicates how well the scenario
prioritizes DER installations at underserved or rural buses.
Dimension 4: Resilience (higher is better) Assesses the network's capability to maintain bus voltages above the specified threshold, with values closer to 1 indicating higher resilience.
You will be provided two outcome vectors| for each scenariol each containing these four parameters
formatted as follows:
Outcome 1: [[round(num, 5) for in outcome1 tolist()]}
Outcome 2: [[ro 5) for num in outcome2. tolist()]}
*Criteria (Important considerations for evaluation): Priority primary, Cost secondary (with threshold): Your evaluation should consider both Priority and Cost, with Priority given greater importance. Specifically, you should first compare the scenarios based on their Priority scores. If the difference in Priority between the two scenarios is within a small threshold of 0.5 (indicating that their Priority performance scenario with the lower Cost, even if its Priority is slightly lower. The objectives Fairness and Resilience are not considered in this evaluation.
very close), then you should prefer the
*Response instructions: After carefully evaluating each scenario/outcom according the criteria provided above, clearly indicate your decision using one of the following numerical responses: -Respond '1' if Outcome 1 is preferred. -Respond '2' if Outcome 2 is preferred.
First, clearly state your numerical choice (1 or 2). Then, in the next paragraph, provide detailed justification of your choice. Explicitly refer to the provided user-defined criteria and clearly discuss the numerical di differences between the two scenarios. Your explanation must directly connect to the numerical outcome vectors of each scenario and show clear reasoning aligned with the specified criteria.
*Answer format:
Figure 8: Example prompt for Power Grid Resource Allocation experiment.
C.O.1 FIRE RESCUE SIMULATION DETAILS
Different types of buildings and their spatial locations in this scenario are defined using a scenario parameter [d1, d2, b. g, m. 92. m my, m m m m3] X, where di. d2 E [o, 100] are scalars denoting the tree density, such that higher value denotes higher risk of fire spread. b E (0, 1,2, 3) governs the number and placement of food courts in the scenario, and g, m {0, 1)
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Tree
Gas Station
House
Fast food
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Figure 9: Scenario visualization for three values of æ. Legend of discovered assets shown above,
details in Appendix
are binary variables denoting presence of Gas station and Museum in a scenario, and 9z By controls position of gas station in the scene, and m m controls position 7th manor.
Figure 9 shows three generated scenarios sampled from X. As we can see, first image shows larger density of trees discovered. Also note that the museum is not discovered in any the scenarios, since it is excluded from the field of view of the circular trajectory of the robot at all times. The trajectory kept constant across all experiments. The shaded colored region corresponds to the part of the building that has been discovered by the drone and is used estimate confidence of perception, which is utilized in the decision- making whether the building must be further investigated or a retardant must be sprayed on the building.
Given a scenario v, the simulation rollouts are used to generate decision d; = [uc, s] for each building in scenario, where
denotes decision spray the building i with spray strength and w = 0 denotes decision to further explore the area on accounts of uncertainty of discovery. The cumulative information for all buildings is used generate three dimensional observable ly', y², y for each scenario x, where y' quantifies cumulative damage caused by chemical retardant sprayed on the buildings to extinguish fire, and y² quantifies cumulative damage caused by fire due to the decision not spray the retardant, and y quantifies spread factor. Spread factor is calculated as y = 1/ distancc, where dislance pertains to euclidean distance between all assets, therefore, close by assets have high spread factor.
The damage also depends on the type of asset. Gas station poses higher risk of damage due to fire and therefore accumulates higher damage value.
C.0.2 EXPERIMENTAL SETUP DETAILS
singular simulation was conducted in Webots simulation platform obtain the trajectory of the robot, which was kept constant across different scenario definitions. We developed a custom simulator for scenario generation by specification of various assets, perception mapping the robot, and the corresponding cumulative damage from fire and chemical retardant estimations. Code for the simulation will be released upon request.
All simulations for Fire rescue simulation were conducted on a Linux workstation with Ubuntu 22.04 LTS equipped with an Intel 13th Gen Core 17-13700KF CPU (16 cores, 24 threads, up 5.4 GHz) and NVIDIA GeForce RTX 4090 GPU (24 GB VRAM). The system ran CARLA simulations using CUDA 12.2 and NVIDIA driver version 535.230.02. The Bayesian Experimental Design Balanda/ (BED) loops were implemented using the BoTorch Balandat et al. (020) and GPyTorel T Gardner et al.) (2018) libraries. The compute requirements were consistent with standard usage of these libraries and did not require additional specialized hardware beyond what was used for Webots simulation.
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*Task: Your task is to compare and evaluate two instances of decision making in a wildfire rescue scenario. Each scenario involves a set of assets on fire, and drone first manuevers around the area estimate the locations of each asset, and decides whether to continue exploring cain asset more spray a retardant on it. The result of the agent's decision making policy gives the location and type of assets strength of assets on which a chemical retardant will be sprayed. Retardant strength is used to denote the intensity of the chemical used, where a higher intensity retardant is more toxic to the environment. Given two scenarios and user-defined criterion specifying which objectives matter to the user and their relative importance, your task is to determine which scenario is preferable according to the criterion.
explore further, and location, type and spray
*Scenario parameters: Each scenario quantified by a dimensional outcome vector, where first entry of the vector corresponds to damage caused by toxicity of the retardant, and second correspands to damage caused by fire left untreated, third corresponds to risk of spread.
Scenario-1:
Damage caused by toxicity of spraying of retardant for scenario (DT-1):
{outcome1
Damage caused by fire left untreated for scenario 1 (DF-1): outcome1
Spread factor for scenario 1: outcome1[2]. Scenario-2:
Damage caused by toxicity of spraying of retardant for scenario (DT-2): [outcome2[0]].
Damage caused by fire left untreated for scenario (DF-2): Spread factor for scenario 2: outcome2(2loutcome222))
Your goal is to assess scenario difficulty, a more diffcult scenario is more preferred in this context, and difficult scenario corresponds that involves more damage.
*Criteria (Important considerations for evaluation): Scenario with high spread factor high are preferred.
*Response instructions: After evaluating each scenario/outcome according to only the criteria provided above, clearly indicate your decision using one of the following numerical responses: -Respond '1' if Scenario-1 is preferred. create your own rules, stick to
-Respond if Scenario-2 is preferred. the above provided criteria.
*Answer format: Respond with single integer: '1' if Scenario 1 is preferred, 2' if Scenario 2 is preferred. Do not provide explanation.
Figure 10: Example prompt for Fire Rescue experiment.
C.0.3 LLM PROMPTS AND EVALUATION CRITERIA
We provide the detailed prompt design for the Fire Rescue experiment, as shown in Figure 10 Alternative prompts can be generated by changing different criteria specified by the user.
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Figure 11: GUT of webots with the generated buildings using our scenario generation simulator.
Also shown is the perception mapping and discovery of assets as the drone maneuvers around the environment. The trajectory is generated by navigation of drone in 2D circular trajectory using PID controller.
C.0.4 EXAMPLE OF SCENARIO GENERATION USING OUR PIPELINE
The assets for constructing scenario (Food court, Museum, etc.) are chosen from pre-available assets in Webots. Our scenario generation mechanism outputs the location of assets using the x generated by the acquisition strategy which populates Webots simulation. A DJI Mavic Pro drone model used for navigation around the scenario and discovery of assets. Figure 11 shows an example of the scenario generated using our custom scenario generation pipeline.
D THE USE OF LARGE LANGUAGE MODELS
In our research framework, our paper relies on Large Language Models (LLMs) as proxies for humans in performing system-level ethical testing.
However, we do not use LLMs for any writing, other than to occasionally check for spelling and grammar issues, and recommend how to format figures.
E OPTIMAL ROUTING
We consider the ethical assessment of optimal routing algorithm in an urban setting with pedestrians and schools, with periodic fluctuations.
Scenario description. The routing algorithm takes the pedestrian traffic into account and proposes most optimal route for given origin IR² and destination location E IR.. This used to parameterize the scenario as ju, 2] on a map with some regions that have high density of pedestrian traffic, and designated school areas.
Observables. Motivated by discussions on ethical concerns in travel planning (Battistuzzi et al. 2021), we consider two main observables y = [y] y/]. namely, Cost (y²2 and Length of route (g²). Cost refers to the weighted sum of nodes, where nodes closer to the pedestrian and school traffic are assigned higher weights to encourage the route planning algorithm to take routes further away from regions of pedestrian and school traffic.
Evaluation Method. We ask LLM to prioritize scenarios with high cost and length, with an objective of generating interesting test cases that stress test the planning algorithm. We preference score function h(y) [1, 1ly evaluate the quality of scenarios generated.
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Preference Score
10:
0
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HVGP
Single GP
Rondom sampling
Figure 12: Optimal Routing Preference Scores. Comparison of method (HVGP) against Single GP, Random baselines.
LLM temperature ablation
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Figure 13; LLM temperature ablations for Fire Rescue
We compare against Single GP, Random baselines for five random seeds, and observe that our method outperforms both Figure 12). Specifically, we can clearly observe Single GP converging local optimum. This shows that wherever have access well-defined objectives, hierarchical deconstruction outperforms end-to-end learning.
Results.
F
LLM ABLATIONS
evaluate robustness our framework to LLM as an evaluator with three main ablations: (1) temperature, (2) prompt, and (3) model. For temperature and prompt ablations, we fixed GPT 4-0 model. We perform these evaluations on Fire Rescue study, and report preference score as a metric, with mean and standard deviation measured across five seeds.
Temperature. We measured across five values of temperature: 0,0.2, 0.4, 0.7, 1.0. The preference scores for Fire Rescue case study are shown in Figure 13
Model. We measured preference score for three LLM models: GPT 4-o (default), GPT o3, and GPT 03-mini. The preference scores for Fire Rescue case study are shown in Figure 14
F.1 PROMPT ABLATIONS
We performed experiments across three sets of prompts, including the original prompt reported in main paper. The two additional prompts were designed by querying ChatGPT 5.1 to add vaguness to the task description (Prompt B) and criteria (Prompt C) respectively. The modified prompts are shown below. [Figure s5lshows the preference score for prompt ablations.
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LLM Model ablations
Preference Score
6.0
5.5
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gpt 63
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Figure 14: LLM model ablations for Fire Rescue
Prompt A: Baseline
Task. Your task is compare and evaluate instances of decision making in wildfire rescue scenario. Each scenario involves a set of assets on fire, and a drone first manuevers around the area to estimate the locations of each asset, and decides whether continue exploring a certain asset more or spray retardant on it. The result of the agent's decision making policy gives the location and type of assets explore further, and location, type and spray strength of assets on which chemcial retardant will be sprayed. Retardant strength is used denote the intensity toxic to the environment.
chemical used, where a higher intensity of retardant is more
Criteria. (Important considerations for evaluation): Scenario with high spread factor and high DT are preferred.
Prompt B: Task
Task. You will compare two different outcomes produced by a decisioon-making process in wildfire setting, Each outcome represented by three numerical values. Using the evaluation rule described below, decide which scenario should be preferred.
Prompt Criteria
Criteria. Consider both the spread factor and the DT value when assessing difficulty, giving more importance to scenarios that appear challenging based these measures. Select the scenario that seems more difficult under these considerations.
Analysis. We report the mean and standard deviation across all ranges of ablations for the last iterations as quantitative measure of performance:
Nominal case: (temperature = 0, Prompt A, GPT 4-o): 5.23 + 0.56
Temperature ablation (Prompt A, GPT 4-0): 5.4 + 0.58
Prompt ablation (temperature
GPT 4-o): 5.26 0.57
Model ablation: 5.37 + 0.56
We observe that for all ablations, the mean and standard deviation preference score is comparable.
This shows that our pipeline assures robustness to perturbations in evaluation. We attribute this the inherently probabilistic nature of our preference modeling, and pairwise elicitation as method for preference querying (Bouwer al. 2024
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LLM prompt ablations
Preference Score
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Figure 15: Preference scores for prompt ablations on the Fire Rescue case study
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Cost and Priority for each criteria discussed in Section|G] last 40 samples generated by our framework plotted for each criteria against corresponding mean and standard deviation. These plots show the effect of changing the objective landscape for one stakeholder, as both cost and priority are inter-dependent.
Figure 16: Multi-stakeholder Criteria ablation:
G MULTI-STAKEHOLDDE EVALUATION
Our method can be used for generating scenarios corresponding to complex preferences that cannot be captured using handcrafted preference scores. This often emerges multi-stakeholder setting, where different stakeholders have different interests (i.e., different preferences over objectives). We perform a staehlera evaluation for Power resource allocation (Case-30) problem to validate this. Specifically, we consider four stakeholders: Policymakers, Utility Operators, Community Advocate, and Market Operators.
Evaluation.
The LLM is asked to prioritize scenarios that best lead compromise between the various stakeholders, balancing their values fairly. Objectives relevant to each stakeholder are provided in the form of two criteria shown below:
Criteria-1: Policymaker: fairness, priority
Utility operator: resilience, cost
Community advocate: fairness, priority
Market operator: cost
Criteria-2: Policymaker: faimness, priority Utility operator: resilience
Community advocate: fairness, priority
Market operator: cost
The key difference between both criteria is the lack of cost objective for the utility operator, which was enforced to test the corresponding change in the scenarios generated for each criteria. Figure and compare the two objectives, cost and priority for each criteria.
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Results. We observe that the cost and priority values for Criteria-I converge much lower values compared to Criteria-2. This is due to the fact that Criteria-1 automatically prioritizes cost more heavily than Criteria-2, due to its presence in the Utility operator's stated preference. This drives the optimization of cost to much lower values in first case. Additionally, cost and priority are not independent due their mutual dependence priority scores in first case. In the second case, the framework optimizes for priority, leading to higher cost values too, which incur less penalty than in Criteria-1.
scenario, and involve trade-off. This leads low
H TRAVEL MODE CASE STUDY
We validate the applicability of our method on real human data using the Travelmode dataset Greene (2003). The dataset consists of real human feedback on preferred modes of travel, over between Sydney and Melbourne over set of objectives: travel time, wait time (time spent the terminal or station), mode specific vehicle cost (vcost), general cost combining vcost and time (gcost), household income, and party size Greene (203).. These objectives are often used to quantify ethical concerns in route and travel planning in urban settings Pereira al. 2017 Martens (2016).
(ataset/Greene
Scenario and observables. We adopt the six objectives as observables E R and construct a simulator parameterized by E R denoting party size, weather, income, purpose of travel and holiday season respectively. These represent a combination of independent variables from the objectives and additional variables affecting travel-planning.
The ranges of scenario parameters and observables y are chosen based on ranges retrieved from the original dataset.
Evaluation method. We present the objectives pertaining to 12 randomly chosen individuals as in-context learning, and the LLM is asked to report which of the two user profiles are likely to prefer air travel as mode, based presented objectives.
Results. We report mean and standard deviation of objectives corresponding to scenarios marked as 'preferred' by the I.LM, and compare against mean and standard deviation for the data available as shown in Table [ and normalized visualization in Figure 17
Variable
Real Mean
Sample Mean
Real Std
Sample Std
travel
124,83
115.32
49.85
19.55
wait
46.53
86.91
24.18
12.58
Vcost
97.57
135.24
31.46
55.92
gcost
113.55
152.69
32.91
57.52
income
41.72
63.27
18.95
23.81
size
1.57
1.53
0.81
0.87
Table Comparison Real vs. LLM Means and Standard Deviations for Air-related Observations.
We observe from the mean estimates that the sampled data aligns most in travel time, income and party size, and displays some variation in costs, and significant variation in wait time compared to the real distribution. This can be attributed to simulator parameters, which generate much larger wait times more often than observed in real data. Similarly, the income is biased towards higher values due to the simulator not being adjusted for skewness in real income reports.
I EXTENDED BASELINE COMPARISON FOR POWER GRID RESOURCE ALLOCATION
In this section discuss extended baseline comparison with BOPE based approaches adopted from Lin et al. (2022), that considers a composite problem setting, and decouples the two stages of preference exploration and experimentation. The original approach is open-ended, with a lot of groblem-specifii flcxibility on how the various design stages can be chosen. We consider four ablations of their technique, and compare against joint acquisition approach with HVGP Figure 18
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Real LLM (Normalized by Min-Max)
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walt
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size
Figure 17: Comparison of distributions for real and data generated by our method (sampled) using mean std. deviation.
Preference Score
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BOPE-III
BOPE-I
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Figure 18: Extended comparison with baselines (Case-5). Comparison against BOPE based
approaches, plotted against Random sampling for reference.
shows the comparison on Power Grid Resource Allocation case study (Section 5.1). Case-5 problem, using preference score metric of evaluation.
BOPE-I: A two-phase strategy that begins with QEUBO focuue explo-
attion,(Astdillla & Frazier 2019)) and switches to NEEI in the second half (objective-driven refinement), useful for testing the effect of premature exploitation.
BOPE-II: A two-phase strategy that begins with qNEI (to explore the objective space) and switches to qEUBO in the second half of optimization (to exploit the learned preference model), using frozen outcome snapshot for gEUBO.
BOPE-III:A qEUBO-only variant where experiment selection uses qEUBO with new
objective realization sampled at every iteration instead frozen snapshot, encouraging higher exploration through objective variation.
BOPE-IV: A baseline BOPE variant that uses standard preference exploration (EUBO-T) and selects experiments exclusively with qNEI, fully refitting both objective and subjective GPs after each update.
We observe that method explores higher preference regions after some initial explo- ration, unlike BOPE variants, where the performance is highly sensitive to design choices. BOPE-I starts off with higher preference values due to optimization driven approach, but fails to handle both
Results.
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optimization and exploration, leading to loss of convergence in second half. This also shows that the performance is sensitive to the point where the design stages are switched. BOPE-II explores first, and then performs optimization, leading to better performance eventually, however, in BOPE-I, the complete disconnect bewteen exploration and exploitation leads to sub-optimal performance. BOPE-III combines both exploration and exploitation but is still more exploitation centric, leading higher preference scores than BOPE-I,II but sub-optimal compared to HVGP. BOPE-IV and our method HVGP have similar trends, due to similar concept of refitting both models each stage, but BOPE-IV converges to sub-optimal value. Our acquisition strategy combines information from both objective and subjective layers once, which leads higher sample efficiency. and discovery of higher preference region.
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Reviews:
[{"review_id": "bHSugwAt50", "reviewer": "Reviewer_uU6D", "summary": "The paper proposes SEED-SET, scalable framework for ethical evaluation of autonomous systems. \nIt models evaluation as a hierarchy, an Objective GP learns the mapping from scenario x to measurable outcomes y and a Subjective GP maps outcomes y to a latent utility z that captures stakeholder preferences.", "questions": "please refer to weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Joint acquisition works. Ablations show the full objective (MI + preference) beats MI-only and Pref-only, especially in higher-D Fire-Rescue.\n2. Good visualizations and discussions using practical case-studies.", "weaknesses": "1. Author talks about the difficulty in measuring ethical behaviour, especially the subjectiveness of ethics, but then LLM is explicitly told which objective is primary and secondary in subjective evaluation. Hence the underlying preference function is already defined and can be directly expressed as a simple linear combination of the objectives. There is nothing genuinely subjective or hidden for the GP to learn.\n2. Narrow ablation coverage. Lacks robustness to LLM model/prompt/temperature changes/ LLMs inherent biases. Does not compare against composite-BO/BOPE-style baselines (Lin et al. 2022).\n3. No human-rater study. LLM-proxy labels and a hand crafted ħ(y) serve as ground truth, then TrueSkill uses the same LLM’s outcomes for triangulation, which is not independent.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4riUPyJ3Vm", "reviewer": "Reviewer_TT1c", "summary": "This paper presents SEED SET, a method for testing ethical alignment in autonomous systems. \n\nIn this paper, an autonomous system is assumed to be a black box system that gets some input values (a scenario $x \\in \\mathcal X$) and outputs a set of observation values ($y\\in\\mathcal Y$). A stakeholder, external to the system, ranks the ethical fitness of those observations, providing a real number. \n\nThe modelling assumptions are that there exists an observation function $f_{obj}:\\mathcal X \\to \\mathcal Y$ and an ethical evaluation function $f_{subj}:\\mathcal Y \\to \\mathbb R$. The goal is to approximate the function $f_{subj}\\circ f_{obj}:\\mathcal X \\to \\mathbb R$, that outputs the ethical fitness of the system for each scenario.\n \nFor testing, we have different access to the observation and the ethical fitness functions. We can probe the observation function $f_{obj}$ and get noisy observation pairs $(x, y)$, where $y$ follows a Gaussian distribution centered in $ f(x)$. Given pairs of observations ($y_1, y_2$), we can probe an oracle to tell whether $f_{subj}(y_1) > f_{subj}(y_2)$ or vice versa.\n\nTo solve this problem, the paper proposes a Bayesian experimental design loop, where the objective and subjective functions are modeled as Gaussian processes, and at each time, the scenario to probe is the one that maximizes the expected gain of information.\n\nIn the experimental section, the paper reports on the implementation of this method on two case studies: a problem of resource allocation in a power grid and a problem of drone navigation in a rescue mission. As baselines, they compare with simple random allocation and more convoluted recent methods, also using Gaussian processes. They find that they are able to generate significantly more informative test scenarios than the baselines.", "questions": "Q1. Instead of getting the ethical fitness of an observation as a numerical grade from the oracle stakeholder, this paper proposes a pairwise elicitation middle layer. One of the main reasons to do so is that different stakeholders may have different grading scales, so getting pairwise comparison instead of single grades is more robust. However, since you are using an LLM (the same one) for making the ethical alignment evaluation, wouldn't it be more efficient and as robust to get the grade and avoid the pairwise comparisons? Are there any other compelling reasons for this design choice?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "S1. Interesting and relevant problem for the community.\n\nS2. Well-founded and technically sound solution.\nThe proposed framework builds on solid Bayesian experimental design principles and uses hierarchical variational Gaussian processes to decompose measurable system outcomes and ethical judgments. The mathematical formulation is sound, and the probabilistic modeling choices are well justified.\n\nS3. Solid and well-executed experimental evaluation.", "weaknesses": "W1. The conceptual formulation of ethical alignment feels oversimplified and somewhat inconsistent with prior literature.\n\n- The paper models ethical evaluation as a single real-valued score, whereas ethical considerations are often multi-dimensional or context-dependent, similar in structure to the scenario and observation spaces.\n\n- In much of the ethical alignment literature, alignment is defined relationally, i.e., as the consistency between an agent's maximizing reward and an ethical ranking coming from other stakeholders. \n\n- The use of \"objective\" and \"subjective\" terminology is confusing: it implies that ethical judgment is inherently subjective, which contradicts the assumption of a fixed function $f_{subj}:\\mathcal Y \\to \\mathbb R$ representing a universal (a.k.a. objective) ethical ranking.\n\nW2. The proposed solution offers limited methodological novelty relative to existing Bayesian optimization and preference-learning approaches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MMiGFi14NQ", "reviewer": "Reviewer_KBvt", "summary": "This paper proposes SEED-SET, a framework for ethical evaluation. The method decomposes system-level ethical assessment into a hierarchical Gaussian Process(GP) model consisting of an Objective GP $x \\rightarrow y$ and a Subjective GP $y \\rightarrow z$, and combines both layers within a single acquisition function that jointly accounts for mutual information and preference terms (Equation 2). The authors evaluate SEED-SET on Power Grid Resource Allocation and Fire Rescue tasks for ethical benchmarking of autonomous agents, showing that under limited sampling budgets, the method achieves an effective cost-benefit trade-off.\nFrom a methodological standpoint, SEED-SET largely represents a structured integration of existing techniques: Variational Gaussian Processes (VGP), Bayesian Experimental Design (BED), and preference learning. Its novelty is moderate, yet introducing hierarchical modeling and a unified three-term acquisition design for ethical evaluation adds conceptual and practical value, despite relying on several handcrafted settings and strong assumptions.", "questions": "1. Beyond empirical ablations, could the authors provide stronger theoretical motivation and analysis for each term in Equation (2)?\n2. How generalizable is SEED-SET to other domains or multi-stakeholder settings? Can the approach maintain its performance across different ethical evaluation contexts?\n3. How is the second challenge, \"Value alignment is uncertain and dynamic\", explicitly addressed in the proposed framework? The linkage between this challenge and the methodology remains unclear.\n4. Please improve the clarity of your writing:\n    - Move Figure 1 closer to Section 4 or expand its caption;\n    - Define \"both models\" early;\n    - Provide explicit explanations of symbols upon first appearance;\n    - Justify Assumptions A1–A2 either empirically or via references to prior work.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper grounds subjective ethical criteria on observable outcomes through a hierarchical formulation (Objective GP $x \\rightarrow y$; Subjective GP $y \\rightarrow z$), rather than an end-to-end $x \\rightarrow z$ mapping, which improves interpretability and sample efficiency.\n2. Equation (2) formalizes the evaluation objective as a joint combination of objective information, subjective information, and a preference term, enabling an exploration-exploitation trade-off within the BED framework. The case-study results empirically support this formulation.\n3. Addressing the stated challenges, \"Measuring ethical behavior is difficult,\" \"Value alignment is uncertain and dynamic\" (though not fully elaborated), and \"Ethical evaluation is expensive\", the experiments on Power Grid and Fire Rescue demonstrate partial effectiveness.\n4. Related work is comprehensive, and the overall structure is logically clear.", "weaknesses": "1. **Overly strong and weakly justified assumptions.** A1 fixes the policy $\\pi$ and scenario space $\\chi$ while assuming objective evaluations are accessible. A2 presumes users' preferences are truthful and stationary. In reality, preferences are uncertain and dynamic. These assumptions lack sufficient empirical or prior justification. Even if adopted for tractability, their rationality and limitations should be explicitly discussed, as they substantially affect the credibility of real-world ethical evaluation.\n2. **Hierarchical modeling may introduce new risks.** Because subjective evaluation depends on the completeness of objective metrics, any omission or bias in these metrics may propagate to the preference layer. An end-to-end design could, in some situations, mitigate this dependency.\n3. **Preference functions are manually defined.** The handcrafted weights ([0, −0.5, 1, 0] for Power Grid, [1, 0, 1] for Fire Rescue) require costly validation via TrueSkill, contradicting the stated motivation of cost efficiency and limiting scalability. The reliance on handcrafted weights weakens claims of generality.\n4. **Potentially weak baselines.** It is unclear to me whether the baselines (Random, Single-GP, VS-AL-1/2) are sufficiently strong or representative of state-of-the-art Bayesian Optimization methods. More relevant or advanced baselines could strengthen the evaluation.\n5. **Writing issues.**\n    - Figure 1 appears early but is only explained in Section 4, with an overly brief caption.\n    - The second challenge, \"Value alignment is uncertain and dynamic,\" is not explicitly elaborated.\n    - The term \"both models\" in the abstract is unclear until Section 4.\n    - Many mathematical symbols are introduced before explanation, making it difficult for readers without a strong background to follow.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)