A book excerpt and interview with Brett Frischmann, co-author of “Re-Engineering Humanity”
By K.N.C.
“We become what we behold. We shape our tools and then our tools shape us.” This truism—by the media-scholar John Culkin about the work of Marshall McLuhan—is more potent than ever in the age of data and algorithms. The technology is having a profound effect on how people live and think.
Some of those changes are documented in “Re-Engineering Humanity” by two technology thinkers from different academic backgrounds: Brett Frischmann is a law professor at Villanova University in Pennsylvania and Evan Selinger teaches philosophy at Rochester Institute of Technology in New York.
Together, they explore how ordinary activities like clicking on an app’s legal terms are made so simple that it “trains” us to not read the contents. Over time, the authors fear that humans will lose their capacity for judgment, discrimination and self-sufficiency. Or, as Douglas Rushkoff, a tech writer, put it: “We should be less scared of robots than of becoming more robotic ourselves.”
The Economist’s Open Future initiative asked Mr Frischmann five questions about these dyspeptic themes. A lightly-condensed excerpt from the book, on Taylorism and its relevance to the algorithmic economy today, appears after his answers.
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The Economist: How is technology "re-engineering" humanity?
Brett Frischmann: Human civilisations have re-engineered humanity for millennia. “Humanity” is who we are, and are capable of being, within our built world. It’s reflected in the world we’re building for ourselves, our children, and future generations. Technology re-engineers humanity in part by affecting human capabilities and in part by shaping and constituting our values, beliefs, and shared commitments.
Our book is about how digital networked technologies coupled with sophisticated social engineering are re-engineering our world and humanity. Like the proverbial frogs in slowly warming water, we’re gradually being led to accept a world governed by supposedly smart tech. For the sake of convenience and cheap bliss, we surrender ourselves, follow scripts, and risk becoming indistinguishable from simple machines.
The Economist: Is it possible to live free of the ubiquitous digital technologies and algorithms that track and influence us? If not, can we really be free?
Mr Frischmann: Nothing is inevitable besides entropy. It’s possible to live free and in diverse ways. It’s increasingly more difficult, however, to leave digital technology aside for significant portions of one’s life; it may require sacrifices that are unbearable for many. The technological, social, economic, educational, political, and cultural systems that many people rely on are interconnected and heavily reliant on digital tech. We need systemic change so that we can live free.
We can, however, find times and spaces within our lives to be free. A first step toward such freedom is to begin looking for opportunities. Then decide for yourself. Just as we teach children to resist peer pressure, we must learn to resist techno-social pressures.
The Economist: One concern is electronic-contracts, which you argue shape human behaviour in troubling ways, and should be reformed. Explain the problem and your solution.
Mr Frischmann: In theory, contract law enables and ought to enable people, first, to exercise their will freely in pursuit of their own ends and, second, to relate to others freely in pursuit of cooperative ends. In practice, electronic contracting threatens autonomy and undermines the development of meaningful relationships built on trust. Optimised to minimise transaction costs, maximise efficiency, minimise deliberation, and engineer complacency, the electronic contracting architecture nudges people to click a button and behave like simple stimulus-response machines.
To recover contract law’s core social functions, we advocate ruling out automatic contracts and favouring contracts based on some degree of deliberation and meaningful relationships; we advocate cutting off hidden side-agreements that perpetuate side-deals in multi-sided markets where the consumer is reduced to a resource to be mined, bought and sold.
The Economist: You advocate building in transactions costs, obfuscation and "seams" within digital systems. How would that help people?
Mr Frischmann: Seamless and friction-free are great optimisation criteria for machines, not for humans. After all, machines are tools that serve human ends. Machines don’t set their objectives. Humans do, or so we hope. To author our lives and not just perform scripts written by others, we need to sustain our freedom to be off—to be free from powerful techno-social engineering scripts. Our proposals help protect such freedom and provide space/opportunities for people to develop capabilities essential to human flourishing.
Flourishing humans need some friction. Friction is resistance. It slows things down. We need opportunities to stop and think, to deliberate and even second-guess ourselves and others. This is how we develop the capacity for self-reflection; how we experiment, learn and develop our own beliefs, tastes, and preferences; how we exercise self-determination. This is free will in action. We’re social beings; meaningful relationships require friction too. It’s how we get to know each other and build trust. Seams are also critical for trusted governance, which is sorely lacking in our digital networked environment.
The Economist: The book turns the Turing Test on its head. Explain your version and why it’s needed.
Mr Frischmann: Alan Turing proposed a test to examine whether a machine can think. He scrutinised the line between humans and machines, focusing on the machine side of the line. We examine the human side, use machines as a baseline, and ask when and how humans behave in a machine-like manner. We begin with different intelligence tests, but extend our analysis to different capacities, such as how we relate to others, as well as the core concepts of free will and autonomy. Our tests are plausible empirical tests and, more importantly, conceptual tools to examine what makes us human and how our humanity is reflected in and affected by the technologies we develop and use.
Many claim this or that tech is dehumanising; such claims are untestable without a baseline. Turing inspired us to use machines as a baseline. Turing tested “humanisation” of machines along a specific dimension—conversational intelligence via text messaging. In a sense, we test “machinisation” of humans along various dimensions. Like Turing, our tests follow a two-step procedure. First, run an experiment—an empirical or thought experiment—to determine if, in some context, humans are behaving like simple machines. If so, pause and look closer at what techno-social engineering is doing to us. The first step is observational; the second step is evaluative.
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Excerpt, lightly condensed, from “Re-Engineering Humanity” (Cambridge University Press, 2018) by Brett Frischmann and Evan Selinger
Taylor’s Scientific Management of Human Beings A paradigm shift occurred at the turn of the twentieth century with the emergence of Frederick Taylor’s theory of scientific management, commonly referred to as Taylorism. Taylor revolutionized the relationships between management and labor.
In his biography of Taylor, Robert Kanigel offers the following description: “Taylor was the first efficiency expert, the original time-and-motion man. To organized labor, he was a soulless slave driver, out to destroy the workingman’s health and rob him of his manhood. To the bosses, he was an eccentric and a radical, raising the wages of common laborers by a third, paying college boys to click stopwatches. To him and his friends, he was a misunderstood visionary, possessor of the one best way that, under the banner of science, would confer prosperity on worker and boss alike, abolishing the ancient class hatreds.”
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Taylor developed his techniques, his theory of scientific management of humans in the workplace, in the late nineteenth century and early twentieth century. Taylor saw substantial inefficiencies in factories and other workplaces, and he attributed many of the inefficiencies to mismanagement of labor. As a young man, Taylor had worked as a shop foreman, attempted to get the most out of his workers, and begun to diagnose the inefficiencies he observed as a product of poorly structured incentives, unmotivated and sometimes shirking laborers, and perhaps most importantly, a tremendous knowledge gap that rendered management ineffective. Managers knew too little about the workers, their tasks, their capabilities, and what motivated them to work.
Over decades and across different workplaces and even industries, Taylor carefully studied workplaces, workers and their work. He examined minute details of tasks performed, and based on the data collected, sought to optimize performance in terms of increased efficiency and productivity. Taylor’s system was generalizable. In other words, his system was not limited to a particular workplace, nor was it limited to any particular set of time and motion studies.
At one level, Taylor’s scientific management system is a type of data-dependent technology. Taylorism is one of the best early examples of data-driven innovation, a concept currently in vogue. Taylor’s system included the techniques for both gathering data and putting such data to use in managing people. Taylor’s system thus encompassed the surveillance techniques employed by the “efficiency experts,” their use of stopwatches and careful visual observation of task performance under varied incentive schemes. For example, he would offer a worker being studied a much higher wage than the prevailing market wage in order to test worker capability and task performance under different conditions, and if possible, push prevailing views about what workers could accomplish and increase productivity. Taylor and his disciples relied on personal observations written in notebooks and careful analysis of various inputs, outputs, processes, and procedures across the many workplaces they studied.
Taylor’s critics emphasized that Taylor’s scientific management was anything but scientific. They alleged (accurately in many cases) that Taylor’s prescriptions for management often had an ad hoc flavor to them. When the data was incomplete, Taylor relied on his own judgment, which amounted to little more than a fudge factor or unwarranted exercise of managerial discretion and could not be considered scientific.
Yet the managerial data gaps would close. Twentieth century technological innovations, ranging from the computer to the camera, have dramatically upgraded the capability of managers to gather, process, evaluate, and act upon data. Not surprisingly, Taylorism spread like wildfire across industries and beyond the factory floor, to hospitals, schools, and various other contexts.
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Consider how Taylorism defines both means and ends. As a technology or management technique or system, Taylorism is obviously branded as a means. The problem to be solved also was unambiguous: inefficiencies plagued the workplace leading to waste and lost productivity. Taylorism and Fordism are famous both for their underlying objective, namely, to increase efficiency, quality, and productivity for the ultimate benefit of managers, owners, and capitalists, and means, specifically by managing factory workers in various ways that get them to behave like machines.
Deeply embedded throughout Taylorism, the ends of productivity and efficiency are not only assumed to be paramount but also to be comprehensible in the language of computation. That is the heart of Taylor’s claim that his system constituted scientific management; it is reflected throughout the system itself. Workers were, in fact, conceived as inputs, cogs, resources, etc.; their work was broken down, analyzed, and programmed. Taylor and his disciples assumed it was all comprehensible in the language of computation. At a fundamental level, Taylorism was a revolutionary system for engineering humans. As Taylor famously declared, “In the past the man was first; in the future the system must be first.”
The assembly line is a particularly salient and culturally recognized example. An assembly line is a manufacturing process involving the progressive assembly of parts into a whole product, where the semi-finished assembly moves from one work station to the next in a linear fashion. While assembly lines predated Taylor and Ford, Ford famously optimized the process for mass production. Fordism combined product standardization, systematized use of assembly lines wherein unskilled laborers used special purpose tools at different stages, and the principle that workers should be paid higher “living wages” to both provide better incentives and enable them to purchase the products they made.
A critically important aspect of this type of techno-social engineering is the environmental nature of the means, the way in which the managers employing the management practices advocated by Taylor (and adapted by Ford) reconstructed the physical and social environments within which their workers worked. Managers could leverage control over the environment to control those within the environment in various subtle but powerful ways. Similar to how the clock reconstructed our environment and us, time and motion studies fueled task and schedule management in the workplace. […]
The factory thus not only produced whatever widget the company eventually sold (e.g., Ford’s automobiles), but it also produced machine-like humans, sometimes referred to as automatons. As Kanigel states: “Both Taylor and Ford raised production, cut costs—and reduced the judgment and skill needed by the average worker. [A Ford plant differed from a Taylorized plant in certain respects.] In either case, the worker was left with eight or ten hours whose minute-by-minute course was more closely prescribed and scrutinized than ever. After Ford and Taylor got through with them, most jobs needed less of everything—less brains, less muscle, less independence.”
Taylorism Criticized Yet Expanding Critics of Taylorism recognized and railed against these effects on workers, but architecting the environment (optimizing it, really) to achieve these particular effects is the technological innovation to note. “The Industrial Revolution turned the timetable and the assembly line into a template for almost all human activities. … [Soon] schools too adopted precise timetables, followed by hospitals, government offices and grocery stores,” notes Yuval Noah Harari in “Sapiens” in 2014. These are interesting examples because they define and are defined by the physical spaces, social institutions, and increasingly technologies that together constitute particular environments designed to engineer humans.
Today, even though the assembly line “defines surprisingly little of modern manufacturing,” (in Kanigel’s words) Taylorism is pervasive. Taylorism had its ups and downs across business schools, management consultancies, and factory floors throughout the twentieth century. Some companies and even industries moved away from it to alternative systems for managing labor. Nonetheless, the basic principles of Taylorism have become deeply embedded in how society conceptualizes all sorts of management, ranging from businesses to government to schools to amateur athletics to child rearing.
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With ever growing data about human labor, task performance, and so on, the trend in workplace surveillance and management has only grown and expanded in scope, and it is likely to continue. Until when? How far can it go? What happens if taken to the extreme? What would it mean to Taylorize human labor fully? One thing it would mean is that we would have accepted, even if only tacitly, the contention that management of human labor is a problem comprehensible in the language of computation. Another thing it would mean is that any boundary around the workplace, employment, or even the idea of work itself would dissipate because human labor is not constrained to any such boundary.
Modern data-driven micro-management of human resources (time, attention, effort, etc.) across various industries is simply a form of Taylorism extended beyond formal employer-employee contexts. Like vehicles, physical space, and computing resources, human physical labor can be optimized for on-demand allocation determined by data and algorithms. The Taylorist vision of efficient management is focused on minimizing costs associated with misallocated or wasted human capital, effort, and attention. Ironically, in the near future, eliminating productive inefficiencies that arise from mismanagement of labor might entail getting rid of human managers altogether and turning instead to smart technologies.
There is no reason to limit technologically-optimized-and-implemented Taylorism to traditional work, however. The logic easily extends to a much wider range of actions that depend upon human labor (time, attention, effort, etc.), whether driving a car, caring for one’s children, exercising our bodies and minds, or any other human activity. In the not so distant future, intelligent technological systems – not necessarily sentient ones – may be deployed to maximize human productivity throughout our lives.
Humans are naturally inefficient. We are often unproductive and costly to sustain. One way to understand the power of the Taylorist logic, particularly as extended beyond the workplace, is that it entails minimization of various costs associated with humans being human. For humanists, this is deeply troubling. Some will emphasize the potential upsides, rooted in increased convenience, entertainment, happiness, and welfare. They’ll argue that on the whole, we’ll all be much better off in a world optimized to deliver cheap bliss.
