Most Artificial Intelligence applications are based on supervised machine
learning (ML), which ultimately grounds on manually annotated data. The
annotation process is often performed in terms of a majority vote and this has
been proved to be often problematic, as highlighted by recent studies on the
evaluation of ML models. In this article we describe and advocate for a
different paradigm, which we call data perspectivism, which moves away from
traditional gold standard datasets, towards the adoption of methods that
integrate the opinions and perspectives of the human subjects involved in the
knowledge representation step of ML processes. Drawing on previous works which
inspired our proposal we describe the potential of our proposal for not only
the more subjective tasks (e.g. those related to human language) but also to
tasks commonly understood as objective (e.g. medical decision making), and
present the main advantages of adopting a perspectivist stance in ML, as well
as possible disadvantages, and various ways in which such a stance can be
implemented in practice. Finally, we share a set of recommendations and outline
a research agenda to advance the perspectivist stance in ML.

本文阐述 “数据透视主义” 的概念，提出通过采取人类主体在机器学习过程中所涉及到的知识表达方法，来代替传统的黄金标准数据集。文章探讨了如何在实践中采用透视主义态度来解决主观和客观机器学习任务的问题，并提出相关建议和研究议程。