Disambiguating Algorithmic Bias: From Neutrality to Justice
Disambiguating Algorithmic Bias: From Neutrality to Justice
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DOI：
10.1145/3600211.3604695
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Elizabeth Edenberg;Alexandra Wood
中科院分区：
文献类型：
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作者：
Elizabeth Edenberg;Alexandra Wood
随着算法在相关领域变得无处不在，社会对可能出现歧视性结果的担忧促使人们迫切呼吁解决算法偏差问题。作为回应，涉及计算机科学、法律和伦理学的丰富文献正在迅速激增，以推进设计公平算法的方法。然而，当计算机科学家、法律学者和伦理学家使用“偏见”这个词时，他们的语言往往并不一致。关于社会是否能够或应该解决算法偏见问题的辩论受到对偏见的各种理解的混淆的阻碍，从对标准的中立偏差到由于偏见、歧视和不同待遇而导致的道德问题不公正的例子。这种术语上的混淆阻碍了处理明显的歧视案件的努力。在这篇文章中，我们考察了消除偏见和为正义而设计的不同方法的前景和挑战。虽然这两种方法都有助于理解和解决明显的算法危害，但我们认为，它们也有被利用的风险，最终会转移那些构建和部署这些系统的人的责任。将这一分析应用于最近的生成性人工智能的例子，我们的论点强调了当前评估算法偏差的方法中未见的危险，并指出了如何在生成性人工智能的早期阶段改变解决偏见的方法，以更有力地满足正义的要求。
As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered by conflations of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice.