AI systems have been shown to produce unfair results for certain subgroups of
population, highlighting the need to understand bias on certain sensitive
attributes. Current research often falls short, primarily focusing on the
subgroups characterized by a single sensitive attribute, while neglecting the
nature of intersectional fairness of multiple sensitive attributes. This paper
focuses on its one fundamental aspect by discovering diverse high-bias
subgroups under intersectional sensitive attributes. Specifically, we propose a
Bias-Guided Generative Network (BGGN). By treating each bias value as a reward,
BGGN efficiently generates high-bias intersectional sensitive attributes.
Experiments on real-world text and image datasets demonstrate a diverse and
efficient discovery of BGGN. To further evaluate the generated unseen but
possible unfair intersectional sensitive attributes, we formulate them as
prompts and use modern generative AI to produce new texts and images. The
results of frequently generating biased data provides new insights of
discovering potential unfairness in popular modern generative AI systems.
Warning: This paper contains generative examples that are offensive in nature.

AI 系统在特定人群中产生不公平结果，需要理解特定敏感属性上的偏见。本文通过发现交叉敏感属性下多样化的高偏见子群体，提出了一种基于偏见引导的生成网络（BGGN）。通过将每个偏见值视为奖励，BGGN 可以高效地生成高偏见的交叉敏感属性。实验证明 BGGN 在现实世界的文本和图像数据集上具有多样性且高效的发现能力。进一步评估生成的未见但可能存在不公平的交叉敏感属性，我们将其建模为提示，并使用现代生成型人工智能生成新的文本和图像。频繁生成偏见数据的结果为发现流行现代生成型人工智能系统中潜在的不公平提供了新的见解。警告：本文包含具有冒犯性的生成示例。

交叉不公平发现

Intersectional Unfairness Discovery

State-of-the-art AI models largely lack an understanding of the cause-effect
relationship that governs human understanding of the real world. Consequently,
these models do not generalize to unseen data, often produce unfair results,
and are difficult to interpret. This has led to efforts to improve the
trustworthiness aspects of AI models. Recently, causal modeling and inference
methods have emerged as powerful tools. This review aims to provide the reader
with an overview of causal methods that have been developed to improve the
trustworthiness of AI models. We hope that our contribution will motivate
future research on causality-based solutions for trustworthy AI.

本文综述了因果方法对于提高可信 AI 解决方案的应用和发展，因为现有的 AI 模型大多缺乏对人类真实世界理解的因果关系的认识，从而导致了模型泛化性能差、不公平以及难以解释等问题。