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  • 标题:Algorithms as discrimination detectors
  • 本地全文:下载
  • 作者:Jon Kleinberg ; Jens Ludwig ; Sendhil Mullainathan
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2020
  • 卷号:117
  • 期号:48
  • 页码:30096-30100
  • DOI:10.1073/pnas.1912790117
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity than is usually possible with human decision making, and this specificity makes it possible to probe aspects of the decision in additional ways. With the right changes to legal and regulatory systems, algorithms can thus potentially make it easier to detect—and hence to help prevent—discrimination.
  • 关键词:machine learning ; algorithms ; discrimination
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