Negative Utilitarianism

Negative utilitarianism is a branch of utilitarian ethics holding that the primary moral obligation is to minimize suffering and harm rather than to maximize happiness or well-being. Where classical utilitarianism treats pleasure and pain as symmetrical forces to be balanced, negative utilitarianism assigns greater moral weight to the elimination of negative experiences. This asymmetry has practical consequences: a negative utilitarian framework would, for instance, prioritize preventing catastrophic harm even at the cost of forgoing significant potential benefits.

In the context of AI ethics and alignment research, negative utilitarianism surfaces as one candidate framework for specifying what AI systems should optimize for. Researchers concerned with existential and catastrophic risk often find negative utilitarian reasoning compelling, since it naturally motivates strong constraints against outcomes involving large-scale suffering—such as misaligned AI systems causing widespread harm. It also informs harm-minimization principles in fairness and safety research, where avoiding discriminatory or dangerous outputs is treated as more urgent than maximizing model performance or user satisfaction.

The framework connects directly to debates in AI value alignment about how to formally represent human values in objective functions. A reward function shaped by negative utilitarian principles would penalize harmful outcomes more heavily than it rewards beneficial ones, reflecting the intuition that causing harm is morally worse than failing to provide an equivalent benefit. This asymmetric weighting has implications for how safety constraints are designed and how tradeoffs between capability and risk are evaluated.

Despite its intuitive appeal in safety-critical contexts, negative utilitarianism faces philosophical challenges, including the so-called "world destruction" objection—the concern that taken to its logical extreme, eliminating all suffering might justify eliminating all sentient life. In practice, AI ethics researchers tend to draw selectively on negative utilitarian intuitions rather than adopting the framework wholesale, using it to motivate harm-avoidance priorities while combining it with other ethical considerations to avoid pathological conclusions.