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Full-Text Articles in Law
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 …
Data-Driven Discrimination At Work, Pauline T. Kim
Data-Driven Discrimination At Work, Pauline T. Kim
AI-DR Collection
A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired, or promoted. Although data algorithms can help to avoid biased human decision-making, they also risk introducing new sources of bias. Algorithms built on inaccurate, biased, or unrepresentative data can produce outcomes biased along lines of race, sex, or other protected characteristics. Data mining techniques may cause employment decisions to be based on correlations rather than causal relationships; they may obscure the basis on which employment decisions are made; and they may further exacerbate inequality because error detection is limited and feedback …