How do we prepare for the upcoming age of automation? It’s a question that’s difficult to answer, and frankly, one that economists Andy Sumner and Lukas Schlogl think we haven’t spent enough time pondering.
In a recently published paper from the pair, they discuss the potential effects that artificial intelligence (AI) could have on the global labor markets and posit a few solutions that could help make the incoming transition a little easier.
Getting Comfortable With Uncertainty
Is the dialogue of whether robots will take our jobs taking up too much of our time? That’s one of the conclusions that Sumner and Schlogl reach in their new paper for U.S. think tank, the Center for Global Development. The duo go on to discuss that, instead of focusing on how many jobs automation may disrupt or destroy, we should turn our sights to solutions for the potential problem.
The economists say that it’s impossible to really know how many jobs will be affected by AI development. But one thing’s for certain — there will be significant effects. This will manifest more in developing economies due to their labor markets’ emphasis on manual labor. Like previous studies, Sumner and Schlogl believe that job market polarization is much more likely than mass unemployment.
Basically, there will still be plenty of work to go around, but it will be at opposite ends of a spectrum. On one side, unskilled work will become less consistent and lower-paid. Benefits for these jobs, like health insurance, pensions, or vacations, may be out the window. On the other end, those skilled in knowledge work (think: doctors, lawyers, tech developers in San Francisco) will experience more benefits and better pay.
Trying to Close the Growing Divide
These changes could cause political dissatisfaction due to lower standards of living and less job security. Some researchers have even suggested that we’re already seeing this in U.S. cities where industries have been disrupted by automation — their citizens are more likely to vote Republican.
To offset this possible imbalance, Sumner and Schlogl propose a few solutions. But even they’re skeptical of their validity in tackling the issues at hand. In one corner, they came up with resolutions they consider “quasi-Luddite.” Essentially, these solutions strive to reverse or stall the changes of automation.
Examples of this category would be regulations against automating existing jobs or increased taxes on products produced with robots. These could prove to be difficult to implement; when goods or services become cheaper somewhere besides the area of regulation, it usually doesn’t take long for the consumer to catch on and take their business elsewhere.
Coping With Change
In the other corner, Sumner and Schlogl have what they call “coping strategies.” These concentrate on either re-skilling disrupted workers or providing a sort of economic safety net, like universal basic income (UBI). But the two are quick to point out that each path comes with its unique caveats.
In the case of retraining workers, it’s not entirely clear what skills will be ‘automation-resistant,’ and for how long. It’s also unclear whether it would be monetarily worth it to retrain a person in the middle of their career. Retraining may also be unviable in developing countries where infrastructures for tertiary education aren’t built up as much as their developed counterparts.
Similarly, UBI may also not be sustainable for developing countries. In fact, it may cause the cost of labor to rise, which would make more employers consider utilizing technology in place of humans. Sumner and Schlogl elaborate: “Questions like profitability, labor regulations, unionization, and corporate-social expectations will be at least as important as technical constraints in determining which jobs get automated.”
So, which option does the pair favor? “In the long term, utopian as it may seem now, [there is a] moral case for a global UBI-style redistribution framework financed by profits from … high-income countries.” But the economists admit that it would be “difficult to see how such a framework would be politically enacted.”
Along with a plethora of benefits, AI-fueled automation is sure to bring its fair share of challenges. What do you think is the best option for solving them?Tags: AI, AI and machine learning, AI and ML, AI App Developer, AI app developers, AI applications, automation, automation and employment, job security, mobile app developers San Francisco, san francisco AI app developer, San Francisco AI app developers, San Francisco app development, san francisco mobile app developers, San Francisco mobile app development