Unpacking AI Safety for Enterprises | Cohere

This 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.