North American workers have, for years, feared that their jobs would move to India or China or some other developing country, and those fears have been borne out. Now, however, workers in India, China, etc. worry that the jobs they gained through outsourcing will be snatched away by robots.
This isn’t always a bad thing. My Roomba does nothing other than save me (and my wife) time. Robots and automation are good things, taking over mundane, repetitive tasks, and reducing errors as they do. But they’re not job creators.
In this age of an eroding middle class and sky-high inequality, fears about losing any sort of foothold in the economy, no matter how tenuous it may be, are valid.
The fear of machines dovetails with fears about Big Data. Big Data should prove to be a boon to Artificial Intelligence, creating smarter and smarter robots (and computer systems), and AI will pour more data points into more and more applications. As a result, knowledge workers will, once again, have to rethink how they fit into the digital economy. This doesn’t mean we’ll be displaced (although many of us probably will), but that we’ll need to replace our easily automated skills with ones that machines just can’t accomplish.
At this early stage of AI and robotics, machines still only do what we tell them to. Humans retain the controls, which is a mixed blessing, since humans are so error-prone. If we fall into the trap of comparing ourselves to automation, we come up short – but only because it’s a false comparison.
In the field of data analytics, humans make mistakes all the time, but they also add a level of knowledge and awareness that no robot will be able to match for the foreseeable future.
“Scientists using the same dataset and the same tools may come to different conclusions when analyzing a problem. Sometimes the answer may be open to interpretation, but in many cases, one of the data scientists’ analyses will just be plain wrong,” said Sandy Steier, CEO of 1010data, a provider of Big Data discovery and sharing tools. “Further, even when the data scientist has done everything correctly given the hard data, there are often additional, less tangible factors that need to be considered, the kind of factors that experienced business people just know.”
That last insight, “factors that experienced business people just know,” is one that I suspect will haunt Big Data for a good while. There’s a ton of truth to the “just knowing” of experts. I’ve been a journalist for a couple of decades now, and there are things I just know about storytelling, connections I’ve made and experiences I’ve collected that inform my knowledge on a subconscious level.
The flip side of that is that there are many things we think we “just know,” which we’re completely wrong about. Big Data is very good at exposing these things. After all, in Big Data’s origin story, Moneyball, Oakland A’s GM Billy Beane wondered why scouts just knew that he’d be a pro ballplayer. It was because he simply looked like a ball player. His frame, gate, arm strength, and demeanor all told scouts he’d be a major league star. He wasn’t. When Beane later rose to become the GM of the A’s, he finally asked himself whether he had failed to live up to his abilities, or whether the scouts were just wrong. He turned to data analysis to find out. The answer: the scouts’ assessments were hopelessly clouded by bias. Too often they relied on anecdotes in place of measurable evidence.
Data makes those biases abundantly clear, and, perhaps, that’s what people fear the most about Big Data: it has the potential to show us where we’re wrong and will have the evidence to prove it. No one likes being told they’re wrong, but in the Big Data age, we better start getting used to it.
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