SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior

SafetyAnalyst creates transparent decision-making for AI safety: Unlike existing moderation systems that operate as "black boxes," SafetyAnalyst uses a "harm-benefit tree" that explicitly maps out potential harms and benefits of AI behaviors, making it possible to understand and explain why a particular piece of content was flagged as unsafe.

The system is steerable to reflect community values: The framework uses 28 interpretable weight parameters that can be adjusted to align with different safety standards and community preferences, rather than imposing a one-size-fits-all approach to content moderation—addressing a key gap in current systems.

Performance improvements are significant: SafetyAnalyst achieved an average F1 score of 0.81 on safety classification benchmarks, outperforming existing moderation systems (which averaged below 0.72), suggesting it can more accurately identify unsafe prompts while maintaining interpretability.

The system considers multiple dimensions of harm: Rather than simple yes/no decisions, SafetyAnalyst evaluates potential impacts across likelihood, severity, and immediacy, and considers effects on different stakeholders—providing a more nuanced assessment of safety risks.

Open-source implementation enables broader adoption: The framework was distilled from analysis of 18.5 million harm-benefit features across 19,000 prompts and is available as open-source, potentially allowing organizations to implement more transparent and accountable AI safety practices.