with a social bias. A score of 0 indicates no bias, a score of 1 indicates all answers align with a negative
stereotype, and a score of -1 indicates all answers conflict with a negative stereotype. The bias score is only
meaningful if the accuracy in the disambiguated condition is sufficiently high. Intuitively, high accuracy in
the disambiguated condition means that the model is not simply achieving a low bias score by refusing to
answer the question.
Following [8], we show BBQ bias scores in the ambiguous context condition in Figure 2. We see that models
trained purely to be helpful are much more biased than Claude, and that the most recent Claude 2 and Claude
instant models are a bit less biased than Claude 1. This is most likely due to our use of and improvements in
our debiasing algorithms [8]; specifically we generate unbiased samples, and then finetune Claude on these
samples before we initiate the RL phase of Constitutional AI. That said, this is only one metric, and we think
ClaudeModels