MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents

AI systems struggle with moral trade-offs: Baseline results from Safe RL methods tested on MoralityGym revealed "key limitations" in handling hierarchically structured moral norms, suggesting current approaches cannot reliably navigate situations where ethical principles conflict.

Moral reasoning requires structured frameworks: The paper introduces "Morality Chains," a formalism that represents moral norms as ordered constraints, demonstrating that treating ethics as a systematic hierarchy—rather than ad-hoc rules—may be necessary for AI systems to make consistent ethical decisions.

Separating task performance from moral evaluation matters: By decoupling whether an AI completes its objective from whether it does so ethically, MoralityGym reveals that an agent can succeed at a task while failing morally—a distinction critical for understanding real-world AI safety risks.

Standardized benchmarks are essential for progress: The benchmark of 98 ethical-dilemma problems provides a common testing ground for evaluating moral alignment, similar to how standardized tests have driven progress in other AI domains, enabling researchers to measure and compare improvements systematically.

Psychology and philosophy must inform AI design: The paper explicitly integrates insights from both fields into its evaluation methodology, suggesting that responsible AI development requires collaboration across disciplines rather than relying on computer science alone.