In recent years, there has been a revolution in data-driven policing. With
that has come scrutiny on how bias in historical data affects algorithmic
decision making. In this exploratory work, we introduce a debiasing technique
for place-based algorithmic patrol management systems. We show that the
technique efficiently eliminates racially biased features while retaining high
accuracy in the models. Finally, we provide a lengthy list of potential future
research in the realm of fairness and data-driven policing which this work
uncovered.

近年来，数据驱动的警务出现了一场革命。通过 对历史数据中的偏见如何影响算法决策进行审查。在这项探索性工作中，我们介绍了一种针对基于地点的算法巡逻管理系统的去偏倚技术。我们展示了该技术在保持模型高精度的同时有效消除了种族偏见的特征。最后，我们提供了一份潜在的未来研究清单，涵盖公平和数据驱动的警务领域。

基于地点的算法巡逻管理的去偏技术

A debiasing technique for place-based algorithmic patrol management

Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of
bias present in word embeddings in terms of a single-number metric. However,
such metrics and the related statistical significance calculations rely on
treating pre-averaged data as individual data points and employing
bootstrapping techniques with low sample sizes. We show that similar results
can be easily obtained using such methods even if the data are generated by a
null model lacking the intended bias. Consequently, we argue that this approach
generates false confidence. To address this issue, we propose a Bayesian
alternative: hierarchical Bayesian modeling, which enables a more
uncertainty-sensitive inspection of bias in word embeddings at different levels
of granularity. To showcase our method, we apply it to Religion, Gender, and
Race word lists from the original research, together with our control neutral
word lists. We deploy the method using Google, Glove, and Reddit embeddings.
Further, we utilize our approach to evaluate a debiasing technique applied to
Reddit word embedding. Our findings reveal a more complex landscape than
suggested by the proponents of single-number metrics. The datasets and source
code for the paper are publicly available.

该研究提出了一种基于贝叶斯建模的方法，以更准确地检测词向量中的偏见，并在 Religion、Gender 和 Race 词表上进行了展示，验证了它的有效性。

基于贝叶斯方法的词向量偏差估计不确定性

A Bayesian approach to uncertainty in word embedding bias estimation

An outbreak in the popularity of transformer-based Language Models (such as
GPT (Brown et al., 2020) and PaLM (Chowdhery et al., 2022)) has opened the
doors to new Machine Learning applications. In particular, in Natural Language
Processing and how pre-training from large text, corpora is essential in
achieving remarkable results in downstream tasks. However, these Language
Models seem to have inherent biases toward certain demographics reflected in
their training data. While research has attempted to mitigate this problem,
existing methods either fail to remove bias altogether, degrade performance, or
are expensive. This paper examines the bias produced by promising Language
Models when varying parameters and pre-training data. Finally, we propose a
de-biasing technique that produces robust de-bias models that maintain
performance on downstream tasks.

这篇研究使用变换器语言模型研究了其训练数据所带来的偏见问题，并提出了一种去偏方法，得到的去偏模型在下游任务上保持了良好的表现。

大型语言模型中的公平性偏差和去偏差之旅

A Trip Towards Fairness: Bias and De-Biasing in Large Language Models

Biases in culture, gender, ethnicity, etc. have existed for decades and have
affected many areas of human social interaction. These biases have been shown
to impact machine learning (ML) models, and for natural language processing
(NLP), this can have severe consequences for downstream tasks. Mitigating
gender bias in information retrieval (IR) is important to avoid propagating
stereotypes. In this work, we employ a dataset consisting of two components:
(1) relevance of a document to a query and (2) "gender" of a document, in which
pronouns are replaced by male, female, and neutral conjugations. We
definitively show that pre-trained models for IR do not perform well in
zero-shot retrieval tasks when full fine-tuning of a large pre-trained BERT
encoder is performed and that lightweight fine-tuning performed with adapter
networks improves zero-shot retrieval performance almost by 20% over baseline.
We also illustrate that pre-trained models have gender biases that result in
retrieved articles tending to be more often male than female. We overcome this
by introducing a debiasing technique that penalizes the model when it prefers
males over females, resulting in an effective model that retrieves articles in
a balanced fashion across genders.

研究通过引入性别缓解技术、使用预训练模型以及适配器网络，成功地减轻了性别偏见问题，并提高了信息检索的敏感性。