Bias amplification is a phenomenon in which models increase imbalances
present in the training data. In this paper, we study bias amplification in the
text-to-image domain using Stable Diffusion by comparing gender ratios in
training vs. generated images. We find that the model appears to amplify
gender-occupation biases found in the training data (LAION). However, we
discover that amplification can largely be attributed to discrepancies between
training captions and model prompts. For example, an inherent difference is
that captions from the training data often contain explicit gender information
while the prompts we use do not, which leads to a distribution shift and
consequently impacts bias measures. Once we account for various distributional
differences between texts used for training and generation, we observe that
amplification decreases considerably. Our findings illustrate the challenges of
comparing biases in models and the data they are trained on, and highlight
confounding factors that contribute to bias amplification.

本文研究了文本到图像领域中的偏差放大现象，通过比较训练和生成的图像中的性别比例，发现模型似乎放大了训练数据中存在的性别职业偏见。然而，我们发现放大主要归因于训练字幕和模型提示之间的差异。当我们考虑到训练和生成所使用的文本之间的各种分布差异后，观察到放大程度显著降低。我们的研究结果说明了比较模型和训练数据中的偏见的挑战，并强调了导致偏见放大的混淆因素。

文本生成图像中的偏差放大悖论

The Bias Amplification Paradox in Text-to-Image Generation

Federated Learning (FL) facilitates distributed model development to
aggregate multiple confidential data sources. The information transfer among
clients can be compromised by distributional differences, i.e., by non-i.i.d.
data. A particularly challenging scenario is the federated model adaptation to
a target client without access to annotated data. We propose Federated
Adversarial Cross Training (FACT), which uses the implicit domain differences
between source clients to identify domain shifts in the target domain. In each
round of FL, FACT cross initializes a pair of source clients to generate domain
specialized representations which are then used as a direct adversary to learn
a domain invariant data representation. We empirically show that FACT
outperforms state-of-the-art federated, non-federated and source-free domain
adaptation models on three popular multi-source-single-target benchmarks, and
state-of-the-art Unsupervised Domain Adaptation (UDA) models on
single-source-single-target experiments. We further study FACT's behavior with
respect to communication restrictions and the number of participating clients.

本文提出了 Federated Adversarial Cross Training （FACT）方法来解决非 i.i.d. 数据的情况下，联邦模型适应目标客户端的挑战。通过使用不同领域的源客户端之间的差异来识别目标域中的域漂移，该方法在多源单目标基准测试中优于现有的联邦模型、非联邦模型和单源单目标实验中的最新无监督域自适应模型。

联邦对抗交叉训练（FACT）

FACT: Federated Adversarial Cross Training

The use of automated decision tools in recruitment has received an increasing
amount of attention. In November 2021, the New York City Council passed a
legislation (Local Law 144) that mandates bias audits of Automated Employment
Decision Tools. From 15th April 2023, companies that use automated tools for
hiring or promoting employees are required to have these systems audited by an
independent entity. Auditors are asked to compute bias metrics that compare
outcomes for different groups, based on sex/gender and race/ethnicity
categories at a minimum. Local Law 144 proposes novel bias metrics for
regression tasks (scenarios where the automated system scores candidates with a
continuous range of values). A previous version of the legislation proposed a
bias metric that compared the mean scores of different groups. The new revised
bias metric compares the proportion of candidates in each group that falls
above the median. In this paper, we argue that both metrics fail to capture
distributional differences over the whole domain, and therefore cannot reliably
detect bias. We first introduce two metrics, as possible alternatives to the
legislation metrics. We then compare these metrics over a range of theoretical
examples, for which the legislation proposed metrics seem to underestimate
bias. Finally, we study real data and show that the legislation metrics can
similarly fail in a real-world recruitment application.

本文讨论了 Local Law 144 对于自动决策工具中的偏见检测指标不足的问题，并提出了可能的替代指标。通过对一些理论例子和实际数据的对比，我们发现新的指标更能准确地检测出偏见。