It’s indisputable that technology is displacing many of today’s jobs. The question is: what should, or can, we do about it? This series explores the possible consequences of this shift and how information use and decision making support can, and should, drive a better outcome through enhanced and expanded Business Intelligence. Part 2 looks at the head in the sand reaction.
In Part 1 of this series, I introduced the three common stances that are taken when confronted with the issue of technological unemployment. Let’s take a deeper look at the first of them now.
Head in the sand
Many mainstream technologists and economists suggest that the jobs market is simply going through a period of re-adjustment—albeit a rather large and painful one—as new technology is adopted. This opinion seems founded mostly on the basis that in previous technology revolutions, such as the move from agriculture to industry in the 1800s and the move from industry to services still ongoing, new jobs have always been created to replace those displaced. Of course, the above timeframes apply to Western economies; emerging economies are at different stages in these transitions. The proposed solutions center on improved and ongoing education, as well as skills diversification. The underlying premise is that there exist, or will soon be created, jobs where robots or algorithms cannot perform better, faster and/or especially cheaper than humans.
The history of predictions of what automated software/hardware solutions cannot do gives little confidence, however. To give but one example, in “The New Division of Labor”, in 2004, the authors describe how driving an automobile requires such complex, instantaneous decisions and actions that it would be extremely difficult for a computer ever to handle it; Google debuted its autonomous car within six years. To be fair to the authors, few people actually get the consequences of the exponential growth rate in computing power that doubles every two years or so. Today’s computers are some 30-40 times more powerful and considerably more cost effective than those of 2004. Whether driving cars or analyzing images for cancerous cells, picking goods from warehouse shelves or making evidence-based recommendations or predictions, technology is displacing an ever-increasing number of previously human activities. My recent TechCrunch article gives some idea of the numbers: they’re not pretty.
On the plus side, new job types are indeed being created. However, their numbers seem small in comparison to those being displaced. A brief review of the possible top jobs in the next ten years, including sex workers (!), from three leading futurists does little to convince that the jobs envisaged will replace the some 4 million driving and support jobs threatened by autonomous cars and trucks.
A recent Fortune article offers the more hopeful view that jobs demanding human accountability, collaborative decision making and interpersonal skills will both be in demand and resistant to automation. I will return to this possibility, in conjunction with “real” BI (actually, Business unIntelligence), as key aspects of the (somewhat) utopian stance. However, from a more contrary viewpoint, we also see robotics aimed at displacing roles that demand human empathy and interaction. The US National Science Foundation (NSF) is spending roughly $1.2 million to fund research on how robots could dress the elderly. Meanwhile, SoftBank has created Pepper, “a social robot able to converse with you, recognize and react to your emotions, move and live autonomously”—seriously!
In the head in the sand stance, business intelligence (BI) plays the traditional role for which it is widely criticized in many businesses: as a means of justification of and reporting on maintaining the status quo. There are always facts and figures to be found and trends to be discovered that justify any viewpoint, especially a mainstream, entrenched view. And, who better to do that than those with their heads in the sand and a deep attachment to the mechanistic, overly rational decision making approaches of the past? In these circumstances, BI definitely makes a meaningful contribution for those involved, but it offers nothing to the understanding or solution of the real issue involved here. Namely, from where will the new sources of income emerge that enables the old consumerist wheel turning?
In part 3 of this series, I address one possible outcome of the wheel seizing up: the dystopian stance where the economy crashes and burns.