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Algorithm-augmented work performance and domain experience : the countervailing forces of ability and aversion
Allen, Ryan; Choudhury, Prithwiraj - 2020
Persistent link: https://www.econbiz.de/10012511858
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Data and Democracy at Work: Advanced Information Technologies, Labor Law, and the New Working Class
Rogers, Brishen - 2023
An exploration of how major companies have used advanced information technologies to limit worker power, and how labor law reform could reverse that trend.As our economy has shifted away from industrial production and service industries have become dominant, many of the nation's largest...
Persistent link: https://www.econbiz.de/10014458245
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Which liability laws for artificial intelligence?
Langlais, Eric; Li, Nanxi - 2024
Persistent link: https://www.econbiz.de/10014575638
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Fair and unbiased algorithmic decision making : current state and future challenges
Tolan, Songül - 2018
Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human … overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are …, fairness constraints in algorithms have to be specific to the domains to which the algorithms are applied. In the future …
Persistent link: https://www.econbiz.de/10012059236
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Algorithmic bias and racial inequality : a critical review
Kasy, Maximilian - 2024
Most definitions of algorithmic bias and fairness encode decisionmaker interests, such as profits, rather than the interests of disadvantaged groups (e.g., racial minorities): Bias is defined as a deviation from profit maximization. Future research should instead focus on the causal effect of...
Persistent link: https://www.econbiz.de/10014520756
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Algorithmic Bias and Racial Inequality: A Critical Review
Kasy, Maximilian - 2024
Most definitions of algorithmic bias and fairness encode decisionmaker interests, such as profits, rather than the interests of disadvantaged groups (e.g., racial minorities): Bias is defined as a deviation from profit maximization. Future research should instead focus on the causal effect of...
Persistent link: https://www.econbiz.de/10014533924
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Fair and unbiased algorithmic decision making: Current state and future challenges
Tolan, Songül - 2018
Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human … overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are …, fairness constraints in algorithms have to be specific to the domains to which the algorithms are applied. In the future …
Persistent link: https://www.econbiz.de/10012387323
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Algorithmic bias and the Value Sensitive Design approach
Simon, Judith; Wong, Pak Hang; Rieder, Gernot - In: Internet policy review : journal on internet regulation 9 (2020) 4, pp. 1-16
Recently, amid growing awareness that computer algorithms are not neutral tools but can cause harm by reproducing and …
Persistent link: https://www.econbiz.de/10012499910
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Can unbiased predictive AI amplify bias?
Khan, Tanvir Ahmed - 2023
Predictive AI is increasingly used to guide decisions on agents. I show that even a bias-neutral predictive AI can potentially amplify exogenous (human) bias in settings where the predictive AI represents a cost-adjusted precision gain to unbiased predictions, and the final judgments are made by...
Persistent link: https://www.econbiz.de/10014452605
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Simplification of administrative procedures through fully automated decision-making : the case of Norway
Weitzenboeck, Emily M. - In: Administrative Sciences : open access journal 11 (2021) 4, pp. 1-23
Norway has a high degree of digitalisation. In the public sector, there is a long tradition of automation of parts of … case management. This includes automation of cases where a public sector body makes a so-called individual administrative …
Persistent link: https://www.econbiz.de/10012795657