While much attention in AI safety focuses on preventing human extinction (x-risks), another critical but less explored area involves "s-risks" - scenarios where advanced AI could create enormous amounts of suffering without necessarily causing extinction. Current AI alignment research might be missing important opportunities to reduce these suffering risks by focusing too narrowly on existential threats.

Exploring AI Alignment for Suffering Reduction

One approach could involve systematically evaluating how different AI alignment methods might reduce s-risks compared to their impact on x-risks. This could include:

• Developing frameworks to compare how well various alignment approaches (like interpretability or robustness) address suffering risks
• Identifying which existing techniques show the most promise for preventing catastrophic suffering scenarios
• Proposing new alignment methods specifically optimized for suffering reduction

The key insight is that some alignment approaches might be particularly effective at preventing suffering scenarios, potentially offering greater overall risk reduction than methods focused solely on extinction prevention.

Potential Benefits and Implementation

This approach could benefit future sentient beings by reducing risks of extreme suffering, while also providing AI safety researchers with expanded tools for comprehensive risk mitigation. For implementation, one might start with developing comparison frameworks (Phase 1), then analyze existing approaches (Phase 2), before proposing new s-risk-specific methods (Phase 3). A simpler starting point could focus just on creating the evaluation framework as a minimum viable product.

Relation to Existing Work

This would complement current AI safety research by adding a specialized focus on suffering outcomes. While organizations like MIRI and CHAI focus mainly on existential risks or general value alignment, this approach would specifically account for suffering-minimization as a distinct and important category of risk. It could help identify cases where standard alignment methods might miss important suffering risks, or where specialized techniques could offer better overall protection.

By systematically considering both existential and suffering risks, this approach might help prioritize research directions that offer the greatest combined risk reduction, potentially revealing overlooked opportunities to make future AI systems safer in more comprehensive ways.