Large language models (LLMs) offer significant potential as tools to support
an expanding range of decision-making tasks. However, given their training on
human (created) data, LLMs can inherit both societal biases against protected
groups, as well as be subject to cognitive bias. Such human-like bias can
impede fair and explainable decisions made with LLM assistance. Our work
introduces BiasBuster, a framework designed to uncover, evaluate, and mitigate
cognitive bias in LLMs, particularly in high-stakes decision-making tasks.
Inspired by prior research in psychology and cognitive sciences, we develop a
dataset containing 16,800 prompts to evaluate different cognitive biases (e.g.,
prompt-induced, sequential, inherent). We test various bias mitigation
strategies, amidst proposing a novel method using LLMs to debias their own
prompts. Our analysis provides a comprehensive picture on the presence and
effects of cognitive bias across different commercial and open-source models.
We demonstrate that our self-help debiasing effectively mitigate cognitive bias
without having to manually craft examples for each bias type.

我们介绍了 BiasBuster，这是一个旨在发现、评估和减轻大型语言模型中认知偏见的框架。通过在心理学和认知科学的前期研究基础上开发了一个包含 16,800 个提示的数据集，我们测试了各种减轻偏见的策略，并提出了一种使用大型语言模型来去偏置其自身提示的新方法。我们的分析提供了商业和开源模型中认知偏见存在及其影响的全面图片。我们证明了我们的自助去偏置方法能够有效减轻认知偏见，而无需为每种偏见类型手动制作示例。

LLMs 的高风险决策中的认知偏差

Cognitive Bias in High-Stakes Decision-Making with LLMs

While extremely useful (e.g., for COVID-19 forecasting and policy-making,
urban mobility analysis and marketing, and obtaining business insights),
location data collected from mobile devices often contain data from a biased
population subset, with some communities over or underrepresented in the
collected datasets. As a result, aggregate statistics calculated from such
datasets (as is done by various companies including Safegraph, Google, and
Facebook), while ignoring the bias, leads to an inaccurate representation of
population statistics. Such statistics will not only be generally inaccurate,
but the error will disproportionately impact different population subgroups
(e.g., because they ignore the underrepresented communities). This has dire
consequences, as these datasets are used for sensitive decision-making such as
COVID-19 policymaking. This paper tackles the problem of providing accurate
population statistics using such biased datasets. We show that statistical
debiasing, although in some cases useful, often fails to improve accuracy. We
then propose BiasBuster, a neural network approach that utilizes the
correlations between population statistics and location characteristics to
provide accurate estimates of population statistics. Extensive experiments on
real-world data show that BiasBuster improves accuracy by up to 2 times in
general and up to 3 times for underrepresented populations.

利用 BiasBuster，利用位置特征与人口统计数据之间的相关性提供准确的人口统计数据估计，从而解决使用偏倚数据集进行准确人口统计的问题。在实际数据的广泛实验中，BiasBuster 的准确性平均提高了 2 倍，并对被低估的人群提高了 3 倍。