1. The Machines Are Not So Easy to Ride: Another Take on Automation
- Author:
- Bright Simons
- Publication Date:
- 03-2019
- Content Type:
- Working Paper
- Institution:
- Center for Global Development
- Abstract:
- In 1981, a former sheep farmer took a one-week crash course in computing, had an epiphany, and teamed up with a car tyres millionaire to form DJ AI. DJ AI announced a new artificial intelligence platform for sale at $600 that could build computer programs for customers. They named their game-changer “The Last One.” All users had to do was follow a set of screen menus, plug, and play, and bingo, they could do away with all those pesky system administrators and programmers. $6 million was spent marketing this powerful piece of magic on both sides of the Atlantic, sometimes to comic effect. Such dreams of software building software, literally cutting out the middleman, have recurred regularly since the 1960s in peaks and troughs, but we are still waiting. From the late 90s onwards, however, new data-driven approaches to automation, particularly so-called deep learning, and the involvement of many of the world’s smartest and most loaded companies, have begun to convince many level-headed analysts that this time it is going to be different. The technologies, we are told, can learn, and so it is about time we paid critical attention to the pace at which they are already and could even further turn upside down the world of work as we know it. Some of the most elegant attempts to evaluate and categorise the impact of these new capabilities on employment and income inequality can be found in papers by David Autor and his co-authors on labour market polarisation and the effect of computerisation on the market demand for skills. More than a decade after these papers were written, their core ideas and the schemas they proposed continue to inspire the framing of the issues in influential circles, making them the most cited in their writers’ corpus. The Economist is right to describe Autor’s seminal work as enormously influential. The elegance and rigorous use of data in these two persuasive treatises are not, however, enough to prevent one major convenient generalisation from weakening their key arguments. The generalisation in question emanates from the conflation of several different patterns of computerisation with “automation,” the replacement of human actors in the chain of work, which is then used as a proxy for technology diffusion and infusion into various modes of work, following in a tradition that also encompasses Goldin and Katz’s equally elegant formulation of the automation question as one of a contention between returns to skills versus returns to algorithms. Having taken “automation” as the predominant form in which modern technology manifests itself in the workplace, Autor and his collaborators then proceed to construct a spectrum of possibilities for technology’s infusion: complement, substitute, or bypass (CSB). In the CSB paradigm, modern technology in the workplace tends to complement super-skilled, high-earning, workers, in complex, adaptive, operations, thereby boosting their productivity and bargaining power; substitute for the contribution of most medium-skilled workers in many routine tasks, thus depressing their wage potential; and bypass low-skilled workers, such as drivers, waiters, and janitors, thus rendering their fate somewhat indeterminate even if their numbers grow. It is not difficult to see why Autor et al.’s extensive use of crosswalking across census-based industrial classification schemes and the Dictionary of Occupational Titles should encourage this neat stratification. Once something is coded, it acquires a hardness that confers rigour and opacity.
- Topic:
- Science and Technology, Automation, and Emerging Technology
- Political Geography:
- Global Focus