How algorithmic wage discrimination works

Policy
Veena dubal taxi
Professor Veena Dubal explains How algorithmic wage discrimination works. | Facebook

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Two sets of brothers who both drive for Uber decided to pursue an investigation into whether or not their pay rates varied despite the fact that they did the same amount of labor. When the siblings opened their Uber applications simultaneously while in the same room, they noticed that they were offered practically identical jobs; however, the pay rate for one of the jobs was somewhat greater than the other. 

According to Veena Dubal, a professor at the University of California College of Law, ridesharing applications like Uber and Lyft encourage algorithmic wage discrimination by customizing wages for each driver based on the data they gather from them. The rationale for this is unknown; however, it is believed to cause the problem. Dubal adds that because ridesharing companies view their employees as independent contractors, they are unable to direct their employees as to what they should do or where they should go. Instead, they make use of pay structures to influence their conduct, and they learn everything there is to know about their drivers so that they may tailor their profits. 

These algorithms are able to identify how much a driver is ready to accept for a certain journey, and how much they try to earn on any given day, and then influence their behavior to ensure the company receives the most significant possible profit. This technique, which is also known as digitalized variable compensation, is quickly becoming the new standard in some companies and has begun to attract the attention of regulators. According to the findings of Dubal's investigation into this situation, there is no transparency in the algorithmic pay rates. In contrast to traditional employment environments, such as factories or offices, where there is a legally mandated standard of equal pay for equal effort, the reasoning behind algorithmic pay rates is typically concealed within opaque algorithms. 

These algorithms may, in some situations, be responsible for replicating traditional wage inequalities, which are prohibited by regulations governing employment, so causing women to earn less money than men. Despite the concerns expressed by Dubal, Uber continues to maintain that the personal data of drivers is not used to determine the pay rates for those drivers. 

A spokeswoman for Lyft stated that Dubal's paper is prejudiced and depends on cherry-picked statistics and material that has been discredited anecdotally. The issue is currently being investigated by regulators, including by the Federal Trade Commission, which is looking into whether or not algorithmic wage discrimination violates antitrust rules. Dubal, on the other hand, is of the opinion that variable digitized compensation might not be breaking the law if there is no finding of an antitrust infringement.

To summarize, the proliferation of algorithmic pay rates rose concerns over unequal compensation and discrimination, which led to requests for a greater degree of transparency and regulatory oversight.

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