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AI Safety Framework

2 posts · Jul 2026

What this trail covers

Cohere's platform now includes Safety Modes for configuring and enforcing AI output guardrails, building upon the existing AI Safety Framework. This provides granular control over content generation to mitigate risks associated with harmful or inappropriate content.

All Cohere trails
  1. Unpacking AI Safety for Enterprises | CohereThis post provides a framework for understanding AI safety by breaking it down into seven themes: types of fairness (allocational and representational), individual vs. distributional harms, the role of factors beyond data in AI safety, the importance of use-case context, the disconnect between upstream and downstream harm mitigation, and the challenges of measuring AI safety. It discusses measurement techniques for fairness, the limitations of red teaming for distributional harms, and the amplification effect of biases in LLMs. The post emphasizes that AI safety is not a one-size-fits-all solution and requires context-specific trade-offs.