However, the paper concludes that models like Claude are not suitable for high-stakes decisions, despite the success of the interventions. The researchers argue that the use of models for such decisions should be influenced by governments and societies, and that potential risks should be anticipated and mitigated as early as possible.
Key takeaways:
- Anthropic researchers have found that AI models can be influenced to reduce biases by using "interventions", which are pleas appended to the prompt that instruct the model not to be biased.
- The researchers tested this method on their own language model, Claude 2.0, and found that it significantly reduced discrimination against protected categories like race and gender.
- The researchers caution that while these interventions can be effective, they do not endorse using language models for high-stakes decisions like loan approvals or job applications.
- The researchers emphasize that the appropriate use of models for high-stakes decisions should be influenced by governments and societies as a whole, rather than being made solely by individual firms or actors.