Does AI Qualify for the Job? A Bidirectional Model Mapping Labour and AI Intensities

EasyChair Preprint 2451, version 2

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.

Keyphrases: AI activity, AI and labour, AI benchmarks, AI impact on jobs, AI intensity, cognitive abilities

@booklet{EasyChair:2451,
  author    = {Fernando Martínez-Plumed and Songül Tolan and Annarosa Pesole and Jose Hernandez-Orallo and Enrique Fernández-Macías and Emilia Gómez},
  title     = {Does AI Qualify for the Job? A Bidirectional Model Mapping Labour and AI Intensities},
  howpublished = {EasyChair Preprint 2451},
  year      = {EasyChair, 2020}}