标题： 超越伤害。 对自然语言处理研究如何应对歧视的批判性综述 显示英文标题

标题： Moving beyond harm. A critical review of how NLP research approaches discrimination

摘要： 如何在自然语言处理技术的背景下避免歧视是该领域的主要挑战之一。 我们提出，对问题采取不同的、更有依据的表述方式可能有助于找到更有效的解决方法。 在论文的第一部分，我们报告了一个案例研究：对2022年发表在ACL文集上的关于自然语言处理系统歧视行为的论文进行定性回顾。 我们发现，该领域（i）仍然强烈关注算法歧视的技术解决方案，（ii）在对其伦理或规范性术语的坚实基础上存在困难。 此外，这种术语非常有限，主要集中在“伤害”和“偏见”这两个术语上。 在论文的第二部分，我们论证说，解决后者的问题可能有助于解决前者的问题。 将算法歧视视为技术问题的理解反映并被当前使用的术语所再现。 “伤害”和“偏见”的概念暗示了对歧视问题的一种狭隘的界定，即作为系统与用户界面之间的问题。 我们认为，辩论应该以“不公正”作为核心概念，而不是“伤害”。 这将迫使我们将算法歧视的问题理解为一个系统性问题。 从而，它将拓宽我们对使自然语言处理技术参与歧视的复杂互动的视角。 通过这种视角的提升，我们可以考虑解决方案的新角度。 显示英文摘要

摘要： How to avoid discrimination in the context of NLP technology is one of the major challenges in the field. We propose that a different and more substantiated framing of the problem could help to find more productive approaches. In the first part of the paper we report on a case study: a qualitative review on papers published in the ACL anthologies 2022 on discriminatory behavior of NLP systems. We find that the field (i) still has a strong focus on a technological fix of algorithmic discrimination, and (ii) is struggling with a firm grounding of their ethical or normative vocabulary. Furthermore, this vocabulary is very limited, focusing mostly on the terms "harm" and "bias". In the second part of the paper we argue that addressing the latter problems might help with the former. The understanding of algorithmic discrimination as a technological problem is reflected in and reproduced by the vocabulary in use. The notions of "harm" and "bias" implicate a narrow framing of the issue of discrimination as one of the system-user interface. We argue that instead of "harm" the debate should make "injustice" the key notion. This would force us to understand the problem of algorithmic discrimination as a systemic problem. Thereby, it would broaden our perspective on the complex interactions that make NLP technology participate in discrimination. With that gain in perspective we can consider new angles for solutions.