New report on how US employers deploy tech and surveillance in their ‘management’ of workers. Hiring software used to score applicandts based on tone and voice and word choices. Algorithms for prediction pregnancy or risk of unionizing. Monitoring speed and pacing faster work are just some examples.
The goal of a new report, showing how US employers use tech against workers, is to give policymakers and other stakeholders an understanding of trends in the data-driven workplace and a framework of the technology rights that workers need and deserve. Despite high unionizing in Europe, a lot of it is still going on in Europe as well and there is new learning of what is in store if we don’t set the red lines in how far we will go.
The report states that dehumanization and automation are not the only path: “With strong worker protections in place, new technology can be put in the service of creating a vibrant and productive economy built on living wage jobs, safe workplaces, and race and gender equity.”
Here are examplew of what is done.
Worker Data Dollection
“Employers can collect an extensive array of data about workers. Some of it is gathered in the workplace, such as computer activity, location in the building, customer ratings, bathroom use, coworker interactions, and smartphone app interactions. Other data is bought from third parties, like social media activity, credit reports, driving history, and consumer activity. Some of this data, such as criminal background checks, has been collected by employers for decades. More recently, as new wearable sensors have become available, employers have partnered with technology vendors and wellness programs to collect more personal biometric and health and wellness data. Methods of data collection range from directly soliciting data from workers (and customers) through surveys or data mining the internet, to microphones embedded in worker badges. Employers may collect worker data themselves, but they may also contract with third-party firms to do so; an entire ecosystem is emerging of businesses engaged in collecting, processing, and selling worker data,” according to the report.
“Electronic monitoring is not new but has become common with the development of passive data collection technologies such as sensors embedded in workplace equipment, devices, and wearables (e.g., wristbands) that can capture a wide range of data on worker location, activities, and interactions with coworkers. Likewise, systems that log keystrokes and capture screenshots enable employers to monitor computer and internet activity. Employers also use GPS technologies embedded in vehicles or in workers’ personal smartphones to monitor their presence on job sites and track their locations while out in the field.”
Algorithms are used to transform input data into technological outputs, which can take the form of everything from promotion recommendations and instructions for delivery drivers, to chatbots and semi-autonomous service robots that complete job tasks.
With care.com clients can view worker profiles to find and select service providers. Worker profiles display performance metrics based on data compiled by the platform, such as customer request response times and customer ratings (which have the potential to perpetuate discrimination against people of color and immigrants in hiring and wage offers).27 These ratings have a significant impact on which workers will be featured in customers’ searches, and therefore on their likelihood of finding work. (from the report)
What the Survillance Is Used For
– Human resource analytics, such as hiring, performance evaluation, and on-the-job training. Employers are increasingly using hiring software to partially or even wholly automate the recruitment process.
– Algorithmic management, such as workforce scheduling, coordination, and direction of worker activities. Employers use electronic monitoring and algorithms to closely track workers’ productivity, set quotas, and make consequential decisions such as discipline or firing based on performance metrics.
– Task automation, where some or even all tasks making up a job are automated using data-driven technologies. Examples are computer analysis of security surveillance footage, semi-autonomous service robots, and self-driving cars.
The company Presto has developed a computer vision system that analyzes video data streams to automatically classify objects and human activities (and therefore flag, for example, long wait times for food or untidy waiting areas). The system uses this analysis to generate scores of likely customer experience. Based on these scores, the system can send real-time notifications to staff so that they can address issues immediately, as well as individual performance reports to managers. (from the report)
According to the report, harms can take the form of work intensification and speed-up; deskilling and automation; hazardous working conditions; growth in contingent work; loss of autonomy and privacy; discrimination; and suppression of the right to organize. Of particular concern is that workers of color, women, and immigrants can face direct discrimination via systemic biases embedded in these technologies, and are also most likely to work in occupations at the front lines of experimentation with artificial intelligence. A future where workers labor in digital sweatshops, micro-managed with no autonomy and under constant pressure, is not too difficult to imagine.