The risk of reinforcing or exacerbating societal biases and inequalities is
growing as generative AI increasingly produces content that resembles human
output, from text to images and beyond. Here we formally characterize the
notion of fairness for generative AI as a basis for monitoring and enforcing
fairness. We define two levels of fairness utilizing the concept of infinite
words. The first is the fairness demonstrated on the generated sequences, which
is only evaluated on the outputs while agnostic to the prompts/models used. The
second is the inherent fairness of the generative AI model, which requires that
fairness be manifested when input prompts are neutral, that is, they do not
explicitly instruct the generative AI to produce a particular type of output.
We also study relative intersectional fairness to counteract the combinatorial
explosion of fairness when considering multiple categories together with lazy
fairness enforcement. Our implemented specification monitoring and enforcement
tool shows interesting results when tested against several generative AI
models.

生成式人工智能越来越多地生成类似于人类输出的内容，从文本到图像等，增加了强化或加剧社会偏见和不平等的风险。我们在此正式刻画生成式人工智能的公平性概念，作为监测和强制公平性的基础。我们利用无限词的概念定义了两个层面的公平性。第一个层面是对生成序列展示的公平性，仅在输出上进行评估，而对提示 / 模型不关心。第二个层面是生成式人工智能模型的固有公平性，要求在输入提示中是中立的情况下也能表现出公平性，即不明确指示生成式人工智能产生特定类型的输出。我们还研究了相对交叉公平性以应对考虑多个类别时公平性的组合爆炸和懒惰公平性强制。我们实施的规范监测和强制工具在测试多个生成式人工智能模型时显示出有趣的结果。