Anthropic Expands Framework for Tackling AI Harms Across Key Risk Areas

As artificial intelligence advances, Anthropic is stepping up its efforts to understand and mitigate a wide range of potential harms—spanning everything from catastrophic biological threats to societal challenges like child safety, misinformation, and fraud.

The company has introduced a more expansive and structured framework to assess AI-related harms, supplementing its Responsible Scaling Policy (RSP)—which focuses on existential risks—with tools aimed at managing everyday and emerging risks alike.

“We believe that considering different types of harms in a structured way helps us better understand the challenges ahead,” Anthropic stated. “It informs our thinking about responsible AI development.”

A Multi-Dimensional Approach to Harm

Anthropic’s framework is designed to guide internal teams in making principled, well-reasoned decisions as AI systems grow more complex. It organizes potential harms into five core dimensions:

• Physical: Bodily safety and health

• Psychological: Mental health and cognitive well-being

• Economic: Financial impacts and risks to property

• Societal: Community-level and institutional effects

• Individual Autonomy: Impacts on personal freedoms and decision-making

For each area, the company evaluates risks based on factors like likelihood, scale, causality, affected groups, duration, technology contribution, and how easily the harms can be mitigated. This method informs both model development and safety practices.

Safety in Practice: From Detection to Enforcement

Anthropic applies its harm framework across a range of safety strategies, including:

• Comprehensive Usage Policies

• Pre- and post-launch evaluations, including red teaming and adversarial testing

• Misuse detection techniques

• Proportional enforcement, from prompt tweaks to account bans

This approach aims to balance safety with practical functionality, especially in everyday use cases.

Real-World Applications of the Framework

Two areas where this framework has already shaped development include:

• Computer Use Capabilities: As models gain the ability to interact with software, Anthropic has assessed risks tied to banking apps and financial software to identify fraud and manipulation, communication tools that influence operations or phishing campaigns, and automation. This led to heightened enforcement thresholds and innovative safeguards like hierarchical summarization, which enables harm detection without compromising user privacy.
  

• Model Response Boundaries: In refining model responses—especially around ambiguous or sensitive prompts—Anthropic focused on maintaining helpfulness while enforcing strict safety standards. For instance, work on Claude 3.7 Sonnet led to a 45% reduction in unnecessary refusals, improving user experience without sacrificing harm prevention.

Anthropic emphasizes that this harm framework is just one part of a broader safety strategy. As AI systems grow more capable, the company expects new, unforeseen challenges to arise and is committed to continually evolving its approach—adapting frameworks, refining methods, and learning from both successes and setbacks.

What This Means

Anthropic’s expanded framework reflects a growing industry recognition that AI safety isn’t just about preventing rare, catastrophic failures—it also means addressing everyday, systemic risks that impact individuals and communities. By categorizing harms across physical, psychological, economic, societal, and autonomy-related dimensions, Anthropic is building a more holistic foundation for AI governance.

This approach also shows a shift toward proportionality in AI oversight: not all harms are equal, and some require stronger interventions than others. Rather than relying on blanket restrictions, the company is working to fine-tune safeguards so that systems remain both useful and responsible.

Importantly, Anthropic acknowledges that this is just one piece of a broader safety puzzle—and that their strategy must keep evolving as AI capabilities grow and new risks surface. They’re committing to ongoing learning and adaptation, which will be crucial as frontier models begin interacting more deeply with society.

How we define and defend against harm today will shape the AI of tomorrow.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.