Abstract
In this paper we develop a framework for analysing the impact of Artificial Intelligence (AI) on occupations. This framework maps 59 generic tasks from worker surveys and an occupational database to 14 cognitive abilities (that we extract from the cognitive science literature) and these to a comprehensive list of 328 AI benchmarks used to evaluate research intensity across a broad range of different AI areas. The use of cognitive abilities as an intermediate layer, instead of mapping work tasks to AI benchmarks directly, allows for an identification of potential AI exposure for tasks for which AI applications have not been explicitly created. An application of our framework to occupational databases gives insights into the abilities through which AI is most likely to affect jobs and allows for a ranking of occupations with respect to AI exposure. Moreover, we show that some jobs that were not known to be affected by previous waves of automation may now be subject to higher AI exposure. Finally, we find that some of the abilities where AI research is currently very intense are linked to tasks with comparatively limited labour input in the labour markets of advanced economies (e.g., visual and auditory processing using deep learning, and sensorimotor interaction through (deep) reinforcement learning).
 This article appears in the special track on AI and Society.
Highlights
There is wide agreement that the latest advances in Artificial Intelligence (AI), driven by rapid progress in machine learning (ML) and its subfields, will have disruptive repercussions on the labour market (Shoham et al, 2018)
We translate the high level categorisation of work tasks to cognitive abilities by sorting each ability according to the objects that they operate on into one of the following three categories: (1) dealing with people: emotion and self-control (EC), mind modelling and social interaction (MS), metacognition and confidence assessment (MC), mind modelling and social interaction (MS); (2) dealing with ideas or information: comprehension and expression (CE), planning, sequential decision-making and acting (PA), memory and processes (MP), attention and search (AS), conceptualisation, learning and abstraction (CL), quantitative and logical reasoning (QL); and (3) dealing with objects or things: sensorimotor interaction (SI), navigation (NV), visual processing (VP), auditory processing (AP)
In this paper we developed a framework that allows for the analysis of the impact of artificial intelligence on the labour market
Summary
There is wide agreement that the latest advances in Artificial Intelligence (AI), driven by rapid progress in machine learning (ML) and its subfields, will have disruptive repercussions on the labour market (Shoham et al, 2018). Previous waves of technological progress have had a sustained impact on labour markets (Autor and Dorn, 2013), yet the notion prevails that the impact of ML will be different (Brynjolfsson et al, 2018). While past technologies could only automate tasks that follow explicit, codifiable rules, ML technologies can infer rules automatically from the observation of inputs and corresponding outputs This implies that ML may facilitate the automation of many more types of tasks than were affected in previous waves of technological progress (Brynjolfsson et al, 2018). Our perception of what AI is able to do is driven by the growing importance of benchmarks in AI (Hernández-Orallo et al, 2017). This classification differentiates between tasks that operate on material
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