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The Future of Work Part 2: The View From the White House

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Top advisors in the Obama Administration published a report titled Artificial Intelligence, Automation, and the Economy in December 2016, which I will call the AI Paper. It’s a statement of the views of the Council of Economic Advisers, the Domestic Policy Council, the Office of Science and Technology Policy, the National Economic Council, and the US Chief Technology Officer, combining their views into a single report. There is a brief Executive Summary which gives a decent overview of the substance of the report, followed by a section on the economics of artificial intelligence technology and a set of policy recommendations. It’s about what you’d expect from a committee, weak wording and plenty of caveats, but there are nuggets worth thinking about.

First, it would be nice to have a definition of artificial intelligence. There isn’t one in this report, but it references an earlier report; Preparing For the Future of Artificial Intelligence, which dances around the issue in several paragraphs. Most of the definitions are operational: they describe the way a particular type of AI might work. But these are all different, just as neural network machine learning is different from rules-based expert systems. So we wind up with this:

This diversity of AI problems and solutions, and the foundation of AI in human evaluation of the performance and accuracy of algorithms, makes it difficult to clearly define a bright-line distinction between what constitutes AI and what does not. For example, many techniques used to analyze large volumes of data were developed by AI researchers and are now identified as “Big Data” algorithms and systems. In some cases, opinion may shift, meaning that a problem is considered as requiring AI before it has been solved, but once a solution is well known it is considered routine data processing. Although the boundaries of AI can be uncertain and have tended to shift over time, what is important is that a core objective of AI research and applications over the years has been to automate or replicate intelligent behavior. P. 7.

That’s circular, of course. For the moment let’s use an example instead of a definition: machine translation from one language to another, as described in this New York Times Magazine article. The article sets up the problem of translation and the use of neural network machine learning to improve previous rule-based solutions. For more on neural network theory, see this online version of Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville. H/T Zach. The introduction may prove helpful in understanding the basics of the technology better than the NYT magazine article. It explains the origin of the term “neural network” and the reason for its replacement by the term “deep learning”. It also introduces the meat on the skeletal metaphor of layers as used in the NYT magazine article.

The first section of theAI Paper takes up the economic impact of artificial intelligence. Generally it argues that to the extent it improves productivity it will have positive effects, because it decreases the need for human labor input for the same or higher levels of output. This kind of statement is an example of what Karl Polanyi calls labor as a fictitious commodity. The AI Paper tells us that productivity has dropped over the last decade. That’s because, they say, there has been a slowdown in capital investment, and a slowdown in technological change. Apparently to the writers, these are unconnected, but of course they are connected in several indirect ways. The writers argue that improvements in AI might help increase productivity, and thus enable workers to “negotiate for the benefits of their increased productivity, as discussed below.” P. 10.

The AI Paper then turns to a discussion of the history of technological change, beginning with the Industrial Revolution. We learn that it was good on average, but lousy for many who lost jobs. It was also lousy for those killed or maimed working at the new jobs and for those marginalized, wounded and killed by government and private armies for daring to demand fair treatment. These are presumably categorized as “market adjustments”, which, according to the AI Paper, “can prove difficult to navigate for many.” P. 12 Recent economic papers show that Wages for those affected by these market adjustments never recover, and we can blame the workers for that: “These results suggest that for many displaced workers there appears to be a deterioration in their ability either to match their current skills to, or retrain for, new, in-demand jobs.” Id.

The AI Paper then takes up some of the possible results of improvements in AI technology. Job losses among the poorest paid employees are likely to be high, and wages for those still employed will be kept low by high unemployment. Jobs requiring less education are likely to be lost, while those requiring more education are likely safer, though certainly not absolutely safe. The main example is self-driving vehicles. Here’s their chart showing the potential for driving jobs that might be lost.

That doesn’t include any knock-on job losses, like reductions in hiring at roadside restaurants or dispatchers.

It also doesn’t include the possible new jobs that AI might create. These are described on pp 18-9. Some are in AI itself, though as the NYT magazine article shows, it doesn’t seem like there will be many. Some new jobs will be created because AI increases productivity of other workers. Some are in new fields related to handling AI and robots. That doesn’t sound like jobs for high school grads. Most of the jobs have to do with replacing infrastructure to make AI work. Here’s Dave Dayen’s description of the need to rebuild all streets and highways so autonomous vehicles can work. Maybe all those displaced 45 year old truck drivers can get a job painting stripes on the new roads. There are no numerical estimates of these new jobs.

The bad news is buried in Box 2, p. 20. Unless there are major policy changes, it’s likely that most of the wealth will be distributed to the rich. And then there’s this:

In theory, AI-driven automation might involve more than temporary disruptions in labor markets and drastically reduce the need for workers. If there is no need for extensive human labor in the production process, society as a whole may need to find an alternative approach to resource allocation other than compensation for labor, requiring a fundamental shift in the way economies are organized.

That certainly opens a new range of issues.

Update: the link to the AI Paper has been updated.

The Future of Work Part 1: John Maynard Keynes

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As the global depression spiraled towards its depths in 1930, John Maynard Keynes wrote a cheerful article on the future of work: Economic Possibilities for our Grandchildren. He argued that it wouldn’t be too long before capital accumulation and technological change would come near to solving the economic problem of material subsistence, of producing enough goods and services to provide everyone with the necessities of life and largely relieving them of the burden of work.

The paper begins with a very brief description of the problems of the time:

We are suffering, not from the rheumatics of old age, but from the growing-pains of over-rapid changes, from the painfulness of readjustment between one economic period and another. The increase of technical efficiency has been taking place faster than we can deal with the problem of labour absorption; the improvement in the standard of life has been a little too quick; the banking and monetary system of the world has been preventing the rate of interest from falling as fast as equilibrium requires.

This statement anticipates the views of Karl Polanyi in The Great Transformation, and of Hannah Arendt in The Origins of Totalitarianism. They argue persuasively that massive technological changes led to changes in social structures which were profoundly upsetting to large numbers of people. Polanyi says that a decent society would take steps to relieve people of these stresses, perhaps by forcing a slower pace of change, or perhaps by legislation to protect the masses. Arendt claims that for a while, imperialism offered a solution by absorbing some of the excess workers. Both believed that the stresses of constant change and displacement of workers played an important role in the rise of fascism.

Keynes then points out the history of growth in world output. From the earliest time of which we have records, he says, to the early 1700s, there was little or no change in the standard of life of the average man. There were periods of increase and decrease, but the average was well under .5%, and never more than 1% in any period. The things available at the end of that period are not much different from those available at the beginning. He argues that growth began to accelerate when capital began to accumulate, around 1700.

It’s interesting to note that this sketch of economic history accords nicely with that provided by Thomas Piketty in Capital In The Twenty-First Century. This is Piketty’s Table 2.5. Compare this with Figure 2.4, The growth rate of world per capita output since Antiquity until 2100.

Keynes argues that since 1700 there has been a great improvement in the lives of most people, and there is every reason to think that will continue. Certainly there was the then current problem of technological unemployment, with technology displacing people faster than the it was creating new jobs. But he says it is reasonable to think that in 100 years, by 2030, people will be 8 times better off, absent war and other factors. He says there are two kinds of needs, those that are absolute, and those with the sole function of making us feel superior to others. The latter may be insatiable, he says, but the former aren’t, and we are getting closer to satisfying them. In so doing, we are getting close to solving the ancient economic problem: the struggle for subsistence.

That problem is indeed ancient. It shows up in Genesis, 3:17. Adam and Eve have eaten the fruit of the Tree of Knowledge of Good and Evil, and the Almighty punishes Adam with these words:

To Adam he said, “Because you listened to your wife and ate fruit from the tree about which I commanded you, ‘You must not eat from it,’ “Cursed is the ground because of you; through painful toil you will eat food from it all the days of your life.

To be relieved of this ancient curse should be a wonderful thing. Keynes doesn’t think it will be an easy transition though. The struggle for subsistence is replaced by a new problem: how to use the new freedom, how to use the new-found leisure. He thinks people will have to have some work, at least at first, to give us time as a species to learn to enjoy leisure. He thinks that those driven to make tons of money will be seen once again in moral terms: as committing the sin of Avarice. They will be ignored or controlled in the interests of the rest of us.

As it turns out, this wasn’t one of Keynes’ better predictions. It isn’t clear that there is such a thing as a minimum absolute needs, for example, and technology has not yet removed the need for all work. Still, the goal of solving the economic problem seems sensible, and his discussion of the problems of a possible transition seems accurate.

People want to work, and they want everyone else to work too. There have been a number of reported interviews with Trump voters, many of who claim that this has become a give-away society. People complain that it pays better to be out of work than in work because of all the free stuff you get, health care (Medicare), free phones, food stamps, SSDI, free housing and so on, so they voted for Trump thinking he’d fix it so that only the deserving poor would get that free stuff. They think people don’t want to work, which feels like projection, and if they have to work, everyone should. Work has a number of social benefits, including a sense of purpose, responsibility, and pride. How are these to be handled in Keynes’ Eden?

The pace of technological change has picked up. It not only affects blue-collar workers, it’s starting to hit on doctors, lawyers and even translators. Here’s an article on improvements in translation based on neural network machine learning from the New York Times Magazine; and here’s a report from the White House on the impact of artificial intelligence on jobs. And here’s an article in the NYT’s Upshot column discussing the White House Report, and a rebuttal from Dean Baker.

These problems are crucial to the future of democracy. They concern the nature of our institutions and our social structures, as well as questions about our nature as human beings. I’ll take these up in more detail in future posts in this series.

Update: Here’s a link to the Keynes paper discussed in this post.