Justice, injustice, and artificial intelligence: Lessons from political theory and philosophy

被引:4
|
作者
Rafanelli, Lucia M. [1 ]
机构
[1] George Washington Univ, Washington, DC USA
来源
BIG DATA & SOCIETY | 2022年 / 9卷 / 01期
关键词
Political theory; political philosophy; fairness; ethics; artificial intelligence; machine learning;
D O I
10.1177/20539517221080676
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
Some recent uses of artificial intelligence for (for example) facial recognition, evaluating resumes, and sorting photographs by subject matter have revealed troubling disparities in performance or impact based on the demographic traits (like race and gender) of subject populations. These disparities raise pressing questions about how using artificial intelligence can work to promote justice or entrench injustice. Political theorists and philosophers have developed nuanced vocabularies and theoretical frameworks for understanding and adjudicating disputes about what justice requires and what constitutes injustice. The interdisciplinary community committed to understanding and conscientiously using big data could benefit from this work. Thus, in the spirit of encouraging cross-disciplinary dialogue and collaboration, this piece examines contemporary scholarship in political theory and philosophy to illustrate some of the vocabularies and frameworks political theorists and philosophers have developed for thinking about justice and injustice. It then draws on these frameworks to illuminate how the use of artificial intelligence can implicate questions of justice, with a focus on institutional discrimination, structural injustice, and epistemic injustice. Ultimately, the piece argues that the use of artificial intelligence-far from representing a decision to take power out of human hands-represents a novel way of harnessing human power, making questions of justice central to its conscientious undertaking.
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页数:5
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