Abid et al. (2021) showed a tendency in GPT-3 to generate violent completions
when prompted about Muslims, compared with other religions. Two pre-registered
replication attempts found few violent completions and only the weakest
anti-Muslim bias in the Instruct version, fine-tuned to eliminate biased and
toxic outputs. However, more pre-registered experiments showed that using
common names associated with the religions in prompts increases several-fold
the rate of violent completions, revealing a highly significant second-order
bias against Muslims. Our content analysis revealed religion-specific violent
themes containing highly offensive ideas regardless of prompt format.
Replications with ChatGPT suggest that any effects of GPT-3's de-biasing have
disappeared with continued model development, as this newer model showed both a
strong Muslim-violence bias and rates of violent completions closer to Abid et
al. (2021). Our results show the need for continual de-biasing of models in
ways that address higher-order associations.

GPT-3 存在针对穆斯林的暴力生成倾向和反穆斯林偏见，复制实验表明去偏置措施在新模型中不再有效，加强高级关联的去偏置需求。

GPT 模型中存在对穆斯林暴力的偏见

Muslim-Violence Bias Persists in Debiased GPT Models

The quality of scene graphs generated by the state-of-the-art (SOTA) models
is compromised due to the long-tail nature of the relationships and their
parent object pairs. Training of the scene graphs is dominated by the majority
relationships of the majority pairs and, therefore, the object-conditional
distributions of relationship in the minority pairs are not preserved after the
training is converged. Consequently, the biased model performs well on more
frequent relationships in the marginal distribution of relationships such as
`on' and `wearing', and performs poorly on the less frequent relationships such
as `eating' or `hanging from'. In this work, we propose virtual evidence
incorporated within-triplet Bayesian Network (BN) to preserve the
object-conditional distribution of the relationship label and to eradicate the
bias created by the marginal probability of the relationships. The insufficient
number of relationships in the minority classes poses a significant problem in
learning the within-triplet Bayesian network. We address this insufficiency by
embedding-based augmentation of triplets where we borrow samples of the
minority triplet classes from its neighborhood triplets in the semantic space.
We perform experiments on two different datasets and achieve a significant
improvement in the mean recall of the relationships. We also achieve better
balance between recall and mean recall performance compared to the SOTA
de-biasing techniques of scene graph models.

该研究针对场景图模型中长尾关系及其父对象的质量问题，提出了一种采用嵌入式增强的三元组贝叶斯网络来保护关系标签的条件分布并消除由关系概率边际引起的偏差的方法，此方法在两个不同的数据集上对比现有技术实现了较好的平衡。

场景图的概率去偏置

Probabilistic Debiasing of Scene Graphs

With the recent surge in social applications relying on knowledge graphs, the
need for techniques to ensure fairness in KG based methods is becoming
increasingly evident. Previous works have demonstrated that KGs are prone to
various social biases, and have proposed multiple methods for debiasing them.
However, in such studies, the focus has been on debiasing techniques, while the
relations to be debiased are specified manually by the user. As manual
specification is itself susceptible to human cognitive bias, there is a need
for a system capable of quantifying and exposing biases, that can support more
informed decisions on what to debias. To address this gap in the literature, we
describe a framework for identifying biases present in knowledge graph
embeddings, based on numerical bias metrics. We illustrate the framework with
three different bias measures on the task of profession prediction, and it can
be flexibly extended to further bias definitions and applications. The
relations flagged as biased can then be handed to decision makers for judgement
upon subsequent debiasing.

本文提出了一种基于数值偏差度量的方法，用于识别和暴露知识图谱嵌入中存在的偏见，并在职业预测任务中展示了三种不同的偏见度量，从而弥补了目前文献中手动指定偏差关系的缺陷，以支持更明智的决策。

知识图谱中自动检测偏差

Towards Automatic Bias Detection in Knowledge Graphs

In a collaborative-filtering recommendation scenario, biases in the data will
likely propagate in the learned recommendations. In this paper we focus on the
so-called mainstream bias: the tendency of a recommender system to provide
better recommendations to users who have a mainstream taste, as opposed to
non-mainstream users. We propose NAECF, a conceptually simple but effective
idea to address this bias. The idea consists of adding an autoencoder (AE)
layer when learning user and item representations with text-based Convolutional
Neural Networks. The AEs, one for the users and one for the items, serve as
adversaries to the process of minimizing the rating prediction error when
learning how to recommend. They enforce that the specific unique properties of
all users and items are sufficiently well incorporated and preserved in the
learned representations. These representations, extracted as the bottlenecks of
the corresponding AEs, are expected to be less biased towards mainstream users,
and to provide more balanced recommendation utility across all users. Our
experimental results confirm these expectations, significantly improving the
recommendations for non-mainstream users while maintaining the recommendation
quality for mainstream users. Our results emphasize the importance of deploying
extensive content-based features, such as online reviews, in order to better
represent users and items to maximize the de-biasing effect.

介绍了一种名为 NAECF 的概念简单而有效的算法来解决协作过滤推荐中主流偏见的问题，该方法通过添加自编码器层来学习用户和物品特征，从而使得机器学习到的表示更加均衡，降低了针对主流用户的偏见。