Four years ago, I wrote a somewhat pessimistic article about Jevons paradox. A 19th-century British economist, William Jevons, noted that as energy-efficient innovations are developed and deployed, energy consumption goes up rather than down. The reason: as energy grows cheaper, we use more of it. We find more and more places to apply energy-consuming devices.
Three years ago, I wrote a somewhat pessimistic article about the future of employment. I argued that smart machines would either: 1) augment knowledge workers, making them much more productive, or; 2) replace knowledge workers altogether. Either way, we would need far fewer knowledge workers.
What if you combine these two rather pessimistic ideas? Oddly enough, the result is a rather optimistic idea.
Here’s an example drawn from a recent issue of The Economist. The process of discovery is often invoked in legal disputes between companies or between companies and government agencies. Each side has the right to inspect the other side’s documents, including e-mails, correspondence, web content, and so on. In complex cases, each side may need to inspect massive numbers of documents to decide which documents are germane and which are not. The actual inspecting and sorting has traditionally been done by highly trained paralegals – lots of them.
As you can imagine, the process is time-consuming and error-prone. It’s also fairly easy to automate through deep learning. Artificial neural networks (ANNs) can study the examples of which documents are germane and which are not and learn how to distinguish between the two. Just turn suitably trained ANNs loose on boxes and boxes of documents and you’ll have them sorted in no time, with fewer errors than humans would make.
In other words, artificial neural networks can do a better job than humans at lower cost and in less time. So this should be bad news for paralegal employment, right? The number of paralegals must be plummeting, correct? Actually no. The Economist tells us that paralegal employment has actually risen since ANNs were first deployed for discovery processes.
Why would that be? Jevons paradox. The use of ANNs has dramatically lowered the obstacles to using the discovery process. Hence, the discovery process is used in many more situations. Each discovery process uses fewer paralegals but there are many more discovery processes. The net effect is greater – not lesser – demand for paralegals.
I think of this as good news. As the cost of a useful process drops, the process itself – spam filtering, document editing, image identification, quality control, etc. – can be deployed to many more activities. That’s useful in and of itself. It also drives employment. As costs drops, demand rises. We deploy the process more widely. Each human is more productive but more humans are ultimately required because the process is far more widespread.
As a teacher, this concept makes me rather optimistic. Artificial intelligence can augment my skills, make me more productive, and help me reach more students. But that doesn’t mean that we’ll need fewer teachers. Rather, it means that we can educate many, many more students. That’s a good thing – for both students and teachers.