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.
Abstract (translated)
随着无人机等自主系统在高风险、以人为中心的领域中的部署日益增多，评估其伦理一致性变得至关重要。这是因为未能进行此类评估会立即对人类生命构成危险，并可能导致长期决策偏差。由于缺乏普遍适用且定义明确的评价指标以及不同利益相关者特有的主观性（这些无法通过分析建模），自动化伦理基准测试的研究尚处于初级阶段。 为解决这一挑战，我们提出了一种名为SEED-SET的贝叶斯实验设计框架，该框架结合了特定领域的客观评估和来自利益相关者的主观价值判断。SEED-SET使用分层高斯过程分别建模这两种类型的评价，并采用一种新颖的方法来根据学习到的质量偏好和符合利益相关者偏好的目标提出有趣的测试候选方案。 我们在两个应用上验证了我们的伦理基准测试方法的有效性，发现该方法优于其他基线方法。我们的方法提供了一个可解释且高效的探索与利用之间的权衡机制，在生成最优测试候选方案方面比基线提高了多达两倍，并在高维搜索空间的覆盖率上提升了1.25倍。 通过SEED-SET框架，我们能够更好地理解自主系统中的伦理问题，并为其制定合适的基准测试策略，从而促进这些系统的安全和可靠部署。
URL
https://arxiv.org/abs/2603.01630
