Does AI qualify for the job? A bidirectional model mapping labour and AI intensities

Publication Type:

Conference Paper

Authors:

Mart'nez-Plumed, Fernando; Tolan, Song'l; Pesole, Annarosa; Hern'ndez-Orallo, José; Fern'ndez-Mac'as, Enrique; G'mez, Emilia

Source:

AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, p.94–100 (2020)

ISBN:

9781450371100

Keywords:

AI benchmarks, AI impact, AI intensity, Labour market, Simulation, tasks

Abstract:

In this paper we present a setting for examining the relation between the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labour and AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples.