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Now Is the Time to Rethink AI, Automation and Employee Rights

The COVID-19 pandemic prompts us to rethink what is considered high- or low-skill work. Whose skills, whose labor and whose hours, exactly, are of value to society? What and who do we value and deem essential, and how do we compensate these workers (e.g., care work or teaching)? 

These questions are particularly pertinent in the context of artificial intelligence and automation. 

The Rush for AI

We are seeing AI technologies increasingly deployed across many parts of society. They are embedded into loan decisions, insurance policy decisions, government services like benefit distribution, spam-folder and auto-correct software, education, search engines and web recommendations, autonomous driving, navigation, precision medicine, policing, security and surveillance, immigration enforcement, military, supply chain management, industry and production and much more.

Around the globe, governments are rushing to mobilize vast amounts of capital to invest into AI innovation. This is often tied to the narrative of AI being central for the Fourth Industrial Revolution. A bulging landscape of national AI strategies has emerged over the past three years that sees vast funding pots being made available for AI research, innovation and national security projects. The federal government of Germany alone has committed 3 billion euros ($3.25 billion) for this purpose, with state governments pitching in additional funds for regional research institutions and public-private partnerships. 

Wrong to See Terminator Vs. Humans

When we look at this global AI landscape, there is something important to note: We see a narrative of AI built on vast (and frankly overstated) expectations of its capabilities. The idea that artificial neural network architecture (and with it, “deep learning”) is the breakthrough technology for creating conscious, or even sentient, machines fuels the looming fear of robots taking our jobs. It prompts us to picture the Terminator, rather than a server farm, in our head.

The Terminator narrative of AI and automation very often depicts “low-skill” or “blue collar” workers as the most likely victims of automation. This framing is not only incorrect, but it is also a strategic distraction from the policy decisions that frame what we see as “skillful” work and what kind of labor we value. 

COVID-19 Shows the Importance of the Human Element

This is thrown into sharp relief in the current global health crisis: If we truly had robots for all our essential “low-skill” services, then these services wouldn’t be on the edge of breaking down to the extent they are now, which shows us how important these job roles really are.

For example, Amazon warehouses are automated to a significant degree, but they are not fully automated. Humans and machines work together and many crucial tasks, such as delivery, are still completed entirely by humans. The key part is that these humans are undervalued and at a much higher risk. 

Their precarity is not only unevenly distributed along the fault lines of well-known inequalities, but it puts us at risk as a society at large. Not having health insurance or not being provided with protective gear fuels the spread of the virus among those workers who form the backbone of what is left of our economy. 

The Wider Context

There is a bigger context to this that we have to consider, and that often gets pushed to the sidelines by the AI hype. First, there is a systemic issue around wage stagnation and automation that extends into important questions around AI. Productivity growth (the proportional change in output growth per unit change in labor output) over the last three decades in the United States has indeed increased due to the introduction of labor-saving technologies, not just AI. Productivity used to grow in tandem with labor compensation; however, that has changed dramatically since the 1970s. Productivity has continued to grow, but wages stagnated.

This change caused by COVID-19 provides a window of opportunity for reconfiguring how we think about society, technology and the economy. 

This means that that laborers lost their stock in productivity and in infrastructure, but they did not necessarily lose their jobs. This shift has coincided with the dismantling of unions, leading to a decline in collective bargaining power and the rise of the gig economy. 

Changes Are Driven by Policy, Not Technology

In the meantime, employers have increased their own stock in crucial infrastructure — just think about Amazon’s cloud empire — but these developments are hardly entirely due to technological innovation and automation. They are the results of policy decisions.

In the U.S., automation is incentivized via tax breaks while human labor remains expensive. So we end up with a situation in which “low-skill” does not equal likeliness of automation — “ease of automation” does.  

“Tax-incentivized ease of automation” is a very different framing than “low-skill.” Contrary to many stories that we hear, tasks that we traditionally value as high-skill are just as much at risk of automation. For example, automating large-scale text analysis through natural language processing technologies is an attractive business proposition for law firms. Writing code, a skill currently valued highly and compensated accordingly, could also be automated.

 This is how automation and the rise of inequality are linked: not through technological change, per se, but political and economic decisions made upstream. Not seeing this relationship clearly pits certain humans — not all humans — against machines in ways that have us focus too much on the machinery and make the wrong decisions around workers’ rights and well-being.

COVID-19 Has Changed the World As We Knew It

This change provides a window of opportunity for reconfiguring how we think about society, technology and the economy. Now is a good moment to draw out strategies for change. We need to stop talking about large-scale work replacements caused by robots, and remind ourselves that technological innovation and change follows policy and investment decisions. The state, not just the private sector, plays a central role here, as economist Mariana Mazzucato has reminded us. 

We need public buy-in (quite literally) for the idea that successful, equitable automation means a sociotechnical system in which workers play a central role, whether through directly or indirectly working with machines, and are compensated accordingly. 

Building Greater Resilience

This is not just a matter of doing the right thing. It is also a matter of getting society and the economy to a point of resilience, which is needed not least to secure the democratic process.  

At the most basic level, wages need to be required to rise in tandem with productivity especially when it comes to “low-skill” work that keeps the most crucial parts of our economy afloat. This means deploying tools that are widely known and yet underused, such as minimum wage and universal health care, as well as worker unions (reestablishment is well underway in the tech worker movement), considerations of universal basic income and public investment in infrastructure. 

Now is the time to make these changes.

Mona Sloane

Sociologist at New York University @mona_sloane

Mona Sloane is a sociologist based at New York University (NYU). She works on inequality in AI design and policy. At NYU, she is a fellow with the Institute for Public Knowledge, The GovLab and the NYU Alliance for Public Interest Technology, as well as adjunct professor at the NYU Tandon School for Engineering.

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