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Humans and machines at work : monitoring, surveillance and automation in contemporary capitalism
Moore P., Upchurch M., Whittaker X., Palgrave Macmillan, Cham, Switzerland, 2018. 260 pp.  Type: Book (978-3-319582-31-3)
Date Reviewed: Dec 12 2019

The unprecedented pace of the so-called fourth industrial revolution, which is characterized by the fusion of technologies and automation with humans, thus forming new physical, digital, and biological spheres for cognitive and manual workplaces, has been fueled recently by significant breakthroughs in machine-learning-based artificial intelligence (AI). The need to collect and analyze huge amounts of data to better understand business and support decision-making processes with more rigorous and evidence-based approaches is also contributing to this current environment.

Although this may sound like a healthy incentive for the symbiosis of humans and machines, this book sheds light on some eye-opening points of view about the double-edged character of the increased digitization of many aspects of our lives. In particular, the book examines the controversial relationship between humans and machines in the workplace. The most interesting perspective is about this relationship within an economic system, namely contemporary capitalism. After reading the book, when it comes to the current advancement of technology and increased automation, many readers will be less enthusiastic and more skeptical than before.

Questions about the purpose of such technologies in the workplace will start buzzing in our minds. Is it really for optimization, so employers can better understand productivity by linking it to human behavior? Or is it more about surveillance and the loss of employee privacy and identity? Is optimizing the health and well-being of those being monitored the ultimate purpose? Or is it maximizing profit and exploitation? Are these app- and platform-based digital economies free of legislative frameworks protecting workers and employees? And do they really boost agility and self-employed people by creating more free time for one’s personal life? Are employees burdened by routine tasks in the workplace that are mentally draining, and will such technologies help employees become more creative and happy? Or are we at the verge of creating an Orwellian society?

Answers to these questions are far from clear. Recent disputes about whether Uber and Deliveroo should be considered as technological platforms or contractual frameworks that obey national and international standards, with regard to the relationship between employer and employee, prove the double-edged character of machines at work. This has also been strengthened by reports about lack of transparency and personal data misuse, such as data raised by radio-frequency identification (RFID) taggers during employee breaks, or the adversarial dissemination of personal data. Are journalists driven by increased pressure to produce stories for a mass audience based on click metrics and other dashboard analytics, or are they driven purely by content?

Things become more scrupulous and obscure when it comes to deploying data processing algorithms because algorithms are behind this wave of automation, be it in the form of recommendation systems when buying products or when personnel are hired or fired. How legitimate is it to hire, fire, or promote personnel based on social ratings, and what do these ratings tell us? How has the recommendation algorithm been designed or trained? Is the algorithm free of bias? The lack of transparency and explainable AI is another major headache related to the harmonic symbiosis between humans and machines, particularly when it comes to cognitive tasks at work.

I highly recommend this book to those seeking answers to these questions, or even just to refresh their point of view when considering this symbiosis between humans and machines in contemporary capitalism.

Not all ten chapters will be of interest to every reader. Hence, the book may be most interesting for those involved in trade unions and social justice, as well as social scientists and economists, not to mention politicians and journalists, rather than technologists per se. As a computer scientist myself, however, I deeply enjoyed reading it, as it encourages me to look into ways of combating the lack of transparency and bias in algorithmic design as a cornerstone for making AI explainable and accountable.

Reviewer:  Epaminondas Kapetanios Review #: CR146810 (2005-0104)
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