While the term `art' defies any concrete definition, this paper aims to
examine how digital images produced by generative AI systems, such as
Midjourney, have come to be so regularly referred to as such. The discourse
around the classification of AI-generated imagery as art is currently somewhat
homogeneous, lacking the more nuanced aspects that would apply to more
traditional modes of artistic media production. This paper aims to bring
important philosophical considerations to the surface of the discussion around
AI-generated imagery in the context of art. We employ existing philosophical
frameworks and theories of language to suggest that some AI-generated imagery,
by virtue of its visual properties within these frameworks, can be presented as
`readymades' for consideration as art.

本文旨在研究生成 AI 系统如何创造艺术品，探讨 AI 系统生成的图像是否被视作艺术品，分析 AI 生成的图像在哲学上的地位并通过视觉属性展现作为 “就绪品” 的艺术性。

AI 生成图像：‘现成品’的新时代

AI-Generated Imagery: A New Era for the `Readymade'

A growing body of literature in fairness-aware ML (fairML) aspires to
mitigate machine learning (ML)-related unfairness in automated decision making
(ADM) by defining metrics that measure fairness of an ML model and by proposing
methods that ensure that trained ML models achieve low values in those
measures. However, the underlying concept of fairness, i.e., the question of
what fairness is, is rarely discussed, leaving a considerable gap between
centuries of philosophical discussion and recent adoption of the concept in the
ML community. In this work, we try to bridge this gap by formalizing a
consistent concept of fairness and by translating the philosophical
considerations into a formal framework for the training and evaluation of ML
models in ADM systems. We derive that fairness problems can already arise
without the presence of protected attributes, pointing out that fairness and
predictive performance are not irreconcilable counterparts, but rather that the
latter is necessary to achieve the former. Moreover, we argue why and how
causal considerations are necessary when assessing fairness in the presence of
protected attributes. We achieve greater linguistic clarity for the discussion
of fairML and propose general algorithms for practical applications.

本文旨在通过形式化一致的公平概念，将哲学讨论转化为 ADM 系统中 ML 模型培训和评估的一种正式框架，并提出了一般算法，以实现 fairML 的更高语言清晰度和实际应用。