Tuesday, December 16, 2014

Empowering People with Machine Learning

From an article in the Wall Street Journal:

When system designers begin a project, they first consider the capabilities of computers, with an eye toward delegating as much of the work as possible to the software. The human operator is assigned whatever is left over, which usually consists of relatively passive chores such as entering data, following templates and monitoring displays.
This philosophy traps people in a vicious cycle of de-skilling. By isolating them from hard work, it dulls their skills and increases the odds that they will make mistakes. When those mistakes happen, designers respond by seeking to further restrict people’s responsibilities—spurring a new round of de-skilling.
Because the prevailing technique “emphasizes the needs of technology over those of humans,” it forces people “into a supporting role, one for which we are most unsuited,” writes the cognitive scientist and design researcher Donald Norman of the University of California, San Diego.
There is an alternative.
In “human-centered automation,” the talents of people take precedence. Systems are designed to keep the human operator in what engineers call “the decision loop”—the continuing process of action, feedback and judgment-making. That keeps workers attentive and engaged and promotes the kind of challenging practice that strengthens skills.
In this model, software plays an essential but secondary role. It takes over routine functions that a human operator has already mastered, issues alerts when unexpected situations arise, provides fresh information that expands the operator’s perspective and counters the biases that often distort human thinking. The technology becomes the expert’s partner, not the expert’s replacement.
Pushing automation in a more humane direction doesn't require any technical breakthroughs. It requires a shift in priorities and a renewed focus on human strengths and weaknesses

Two thoughts come to mind:

First, there's Tyler Cowen's analogy of freestyle chess. He uses this analogy liberally in Average is Over. And the division of labor between human and computer in freestyle chess mirrors the above quote.

Second, I was taught the dichotomy of these two philosophies in the Marine Corps. I enlisted just before 9/11; ten + years of war may have changed the budgetary environment. But at the time, Marine infantry units did not have much of a budget. As a result, we trained ourselves (and our minds) first, and supplemented with what technology we could afford. On occasional training with some other (unnamed) branches of the military, we observed that these other units were awash in technology, helpless without it, and not any better than us with it. (Think fancy GPS versus old GPS + map & compass.)

I believe that the latter thought is an example of another quote from the WSJ article:

If we let our own skills fade by relying too much on automation, we are going to render ourselves less capable, less resilient and more subservient to our machines. 

Something to keep in mind as you're implementing your decision support systems.

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