TL;DR: How do AI systems make moral decisions, and can we influence their ethical judgments? We probe these questions by examining Llama's 70B (3.1 and 3.3) responses to moral dilemmas, using Goodfire API to steer its decision-making process. Our experiments reveal that simply reframing ethical questions - from "harm one to save many" to "let many perish to avoid harming one" - dramatically affects both the model's responses and its susceptibility to steering.

Key Insights:

Model Evolution Matters: The newer Llama 3.3 shows stronger resistance to steering attempts than its predecessor (3.1), suggesting increasingly robust ethical frameworks in newer models.
Harmlessness Runs Deep: Llama 3.3 demonstrates remarkable resistance to suggestions of causing harm, even when presented with potentially greater benefits - likely a result of extensive safety-focused training.
Framing Changes Everything: When we inverted moral dilemmas to focus on inaction rather than action, the model's responses shifted significantly, revealing that AI ethical reasoning is highly sensitive to how questions are posed.

GitHub for Project

Moral Dilemmas in the Age of AI

Would you save five lives by sacrificing one? Should aid organizations accept funding from arms dealers to feed more refugees? These thorny ethical questions exemplify moral dilemmas that have challenged philosophers for centuries (e.g. Mozi, 430 BCE; Hume, 1777). At their heart lie two key principles of utilitarian philosophy: instrumental harm (IH) - the idea that causing harm can be justified to achieve a greater good - and impartial beneficence (IB) - the belief that we should benefit all people equally, regardless of personal connections.

As AI systems grow more capable, understanding how they navigate such moral decisions becomes crucial (Sachdeva and Nuenen, 2025). An AI system that readily accepts collateral damage to achieve its goals could pose catastrophic risks, even if well-intentioned (e.g. McQuillan, 2022; Hendrycks et al 2023). Research by Garcia et al. (2024) demonstrates how this risk may manifest in practice, showing significant gaps between AI systems' moral decision-making and human ethical preferences.

Our study explores how large language models (LLMs) handle these ethical challenges through the lens of model steering; techniques that modify AI behavior without changing the mode...

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