Pre-trained language models (PLMs) have revolutionized both the natural
language processing research and applications. However, stereotypical biases
(e.g., gender and racial discrimination) encoded in PLMs have raised negative
ethical implications for PLMs, which critically limits their broader
applications. To address the aforementioned unfairness issues, we present
fairBERTs, a general framework for learning fair fine-tuned BERT series models
by erasing the protected sensitive information via semantic and fairness-aware
perturbations generated by a generative adversarial network. Through extensive
qualitative and quantitative experiments on two real-world tasks, we
demonstrate the great superiority of fairBERTs in mitigating unfairness while
maintaining the model utility. We also verify the feasibility of transferring
adversarial components in fairBERTs to other conventionally trained BERT-like
models for yielding fairness improvements. Our findings may shed light on
further research on building fairer fine-tuned PLMs.

通过生成对抗网络生成语义和公平感知扰动，通过消除受保护敏感信息来学习公平微调的 BERT 类模型 fairBERTs，在维持模型实用性的同时，解决预训练语言模型中存在的刻板偏见和不公平问题。

fairBERTs：通过语义和公平感知扰动抹除敏感信息

fairBERTs: Erasing Sensitive Information Through Semantic and  Fairness-aware Perturbations

Tackling unfairness in graph learning models is a challenging task, as the
unfairness issues on graphs involve both attributes and topological structures.
Existing work on fair graph learning simply assumes that attributes of all
nodes are available for model training and then makes fair predictions. In
practice, however, the attributes of some nodes might not be accessible due to
missing data or privacy concerns, which makes fair graph learning even more
challenging. In this paper, we propose FairAC, a fair attribute completion
method, to complement missing information and learn fair node embeddings for
graphs with missing attributes. FairAC adopts an attention mechanism to deal
with the attribute missing problem and meanwhile, it mitigates two types of
unfairness, i.e., feature unfairness from attributes and topological unfairness
due to attribute completion. FairAC can work on various types of homogeneous
graphs and generate fair embeddings for them and thus can be applied to most
downstream tasks to improve their fairness performance. To our best knowledge,
FairAC is the first method that jointly addresses the graph attribution
completion and graph unfairness problems. Experimental results on benchmark
datasets show that our method achieves better fairness performance with less
sacrifice in accuracy, compared with the state-of-the-art methods of fair graph
learning. Code is available at: this https URL.

本文提出了一种名为 FairAC 的公平属性补全方法，采用注意力机制来处理属性丢失问题，并同时减少因属性完整性而导致的两种不公平性，通过在基准数据集上进行实验，结果表明我们的方法在公平性性能方面比公平图学习的现有方法表现更好且牺牲较少的准确性。

带有缺失属性的图上的公平属性补全

Fair Attribute Completion on Graph with Missing Attributes

STEALTH is a method for using some AI-generated model, without suffering from
malicious attacks (i.e. lying) or associated unfairness issues. After
recursively bi-clustering the data, STEALTH system asks the AI model a limited
number of queries about class labels. STEALTH asks so few queries (1 per data
cluster) that malicious algorithms (a) cannot detect its operation, nor (b)
know when to lie.

STEALTH 是一种方法，通过递归双向聚类数据并向 AI 模型询问关于类别标签的有限数量的查询来避免恶意攻击和不公平问题。