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Articles 1 - 3 of 3
Full-Text Articles in Law
Future Work, Jeffrey M. Hirsch
Future Work, Jeffrey M. Hirsch
AI-DR Collection
The Industrial Revolution. The Digital Age. These revolutions radically altered the workplace and society. We may be on the cusp of a new era—one that will rival or even surpass these historic disruptions. Technology such as artificial intelligence, robotics, virtual reality, and cutting-edge monitoring devices are developing at a rapid pace. These technologies have already begun to infiltrate the workplace and will continue to do so at ever increasing speed and breadth.
This Article addresses the impact of these emerging technologies on the workplace of the present and the future. Drawing upon interviews with leading technologists, the Article explains the …
Algorithms At Work: Productivity Monitoring Applications And Wearable Technology As The New Data-Centric Research Agenda For Employment And Labor Law, Ifeoma Ajunwa
AI-DR Collection
Recent work technology advancements such as productivity monitoring platforms and wearable technology have given rise to new organizational behavior regarding the management of employees and also prompt new legal questions regarding the protection of workers’ privacy rights. In this Essay, I argue that the proliferation of productivity monitoring applications and wearable technologies will lead to new legal controversies for employment and labor law. In Part I, I assert that productivity monitoring applications will prompt a new reckoning of the balance between the employer’s pecuniary interests in monitoring productivity and the employees’ privacy interests. Ironically, such applications may also be both …
Bias In, Bias Out, Sandra G. Mason
Bias In, Bias Out, Sandra G. Mason
AI-DR Collection
Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit future crime. As many scholars have noted, these algorithms tend to have disparate racial impact. In response, critics advocate three strategies of resistance: (1) the exclusion of input factors that correlate closely with race, (2) adjustments to algorithmic design to equalize predictions across racial lines, and (3) rejection of algorithmic methods altogether.
This Article’s central claim is that these strategies are at best superficial and at worst counterproductive, because the source of racial inequality in risk assessment lies …