An insider can use GANs to poison behavioral worker-selection models in mobile crowdsensing, raising cancellation predictions and cutting victim payments while evading outlier detection.
The Roots of Bias on Uber
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abstract
In the last decade, there has been a growth in, what we call, digitally mediated workplaces. A digitally mediated workplace is one where interactions between stakeholders are primarily managed by proprietary, algorithmically managed digital platform. The replacement of the relationships between the stakeholders by the platform is a key feature of these workplaces, and is a contributing factor to the decrease in contractual responsibilities each stakeholder has to one another. In this paper, we discuss some of the ways in which this structure and lack of accountability serves as a root of, or at least an enabler to, the realization of biases in the ridesharing application Uber, a digitally mediated workplace.
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Poisoning Behavioral-based Worker Selection in Mobile Crowdsensing using Generative Adversarial Networks
An insider can use GANs to poison behavioral worker-selection models in mobile crowdsensing, raising cancellation predictions and cutting victim payments while evading outlier detection.