As language models (LMs) advance, interest is growing in applying them to
high-stakes societal decisions, such as determining financing or housing
eligibility. However, their potential for discrimination in such contexts
raises ethical concerns, motivating the need for better methods to evaluate
these risks. We present a method for proactively evaluating the potential
discriminatory impact of LMs in a wide range of use cases, including
hypothetical use cases where they have not yet been deployed. Specifically, we
use an LM to generate a wide array of potential prompts that decision-makers
may input into an LM, spanning 70 diverse decision scenarios across society,
and systematically vary the demographic information in each prompt. Applying
this methodology reveals patterns of both positive and negative discrimination
in the Claude 2.0 model in select settings when no interventions are applied.
While we do not endorse or permit the use of language models to make automated
decisions for the high-risk use cases we study, we demonstrate techniques to
significantly decrease both positive and negative discrimination through
careful prompt engineering, providing pathways toward safer deployment in use
cases where they may be appropriate. Our work enables developers and
policymakers to anticipate, measure, and address discrimination as language
model capabilities and applications continue to expand. We release our dataset
and prompts at this https URL

使用自然语言生成模型（LM）可能存在潜在的歧视风险，因此需要更好的方法来评估这些风险。本研究通过使用 LM 生成各种潜在的提示语句，并系统性地改变其中的人口统计信息，从而在各种社会决策场景中评估 LM 的潜在歧视影响。结果显示，在某些特定情况下，未进行干预的 Claude 2.0 模型存在积极和消极歧视的模式。通过有效的提示语句工程，我们展示了降低这些歧视的技术，为安全部署 LM 在适当的使用场景提供了路径。这一研究成果使开发人员和决策者能够在语言模型的能力和应用不断扩展的过程中预见、衡量和解决歧视问题。