2014 PolanyisParadoxandtheShapeofEmp

From GM-RKB
Jump to navigation Jump to search

Subject Headings: Polanyi's Paradox.

Notes

  • How disappointing that a labor economist would focus on the computing science topic of what machines still cannot do well enough (driverless cars, warehouse automation) instead of telling us how the many workers who will be made redundant in the near feature will land on their feet. May I offer that the amplification of talented workers in the new well-paying technology-empowered jobs will enable many organizations to shut the entrance to the majority of repurposed applicants? That, the Garry Kasparovs and Ken Jennings of the working world, with access to a modern computing system, will be able to outwork tens of thousands of working drivers, order-pickers, and cashiers who enter technology-focused adult-education programs. I look forward to the reply from Mr. Autor (and other labor economists) about this speculation which falls within his field of expertise.

Cited By

Quotes

Author Keywords

Job polarization, technological change, computerization, job tasks, skill demand.

Abstract

In 1966, the philosopher Michael Polanyi observed, “We can know more than we can tell... The skill of a driver cannot be replaced by a thorough schooling in the theory of the motorcar; the knowledge I have of my own body differs altogether from the knowledge of its physiology." Polanyi's observation largely predates the computer era, but the paradox he identified -- that our tacit knowledge of how the world works often exceeds our explicit understanding -- foretells much of the history of computerization over the past five decades. This paper offers a conceptual and empirical overview of this evolution. I begin by sketching the historical thinking about machine displacement of human labor, and then consider the contemporary incarnation of this displacement -- labor market polarization, meaning the simultaneous growth of high-education, high-wage and low-education, low-wages jobs -- a manifestation of Polanyi's paradox. I discuss both the explanatory power of the polarization phenomenon and some key puzzles that confront it. I then reflect on how recent advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. A key observation of the paper is that journalists and expert commentators overstate the extent of machine substitution for human labor and ignore the strong complementarities. The challenges to substituting machines for workers in tasks requiring adaptability, common sense, and creativity remain immense. Contemporary computer science seeks to overcome Polanyi's paradox by building machines that learn from human examples, thus inferring the rules that we tacitly apply but do not explicitly understand.


References

;

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2014 PolanyisParadoxandtheShapeofEmpDavid H. AutorPolanyi's Paradox and the Shape of Employment Growth2014