AI exposure predicts unemployment risk

The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI...

The spread and rapid development of AI-related technologies are influencing many aspects of our daily lives, from social to educational, including the labour market. Many researchers have been highlighting the key role AI and technologies play in reshaping jobs and their related tasks, either by automating or enhancing human capabilities in the workplace. Can we estimate if, and to what extent, jobs and related tasks are exposed to the risk of being automatized by state-of-th...

AI's impact has traditionally been assessed in terms of occupations. However, an occupation is comprised of interconnected tasks, and it is these tasks, not occupations themselves, that are affected by AI. To evaluate how tasks may be impacted, previous approaches utilized manual annotations or coarse-grained matching. Leveraging recent advancements in machine learning, we replace coarse-grained matching with more precise deep learning approaches. Introducing the AI Impact (A...

The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and ...

The uniqueness of human labour is at question in times of smart technologies. The 250 years-old discussion on technological unemployment reawakens. Prominently, Frey and Osborne (2017) estimated that half of US employment will be automated by algorithms within the next 20 years. Other follow-up studies conclude that only a small fraction of workers will be replaced by digital technologies. The main contribution of our work is to show that the diversity of previous findings re...

The fourth industrial revolution (4IR) is likely to have a substantial impact on the economy. Companies need to build up capabilities to implement new technologies, and automation may make some occupations obsolete. However, where, when, and how the change will happen remain to be determined. Robust empirical indicators of technological progress linked to occupations can help to illuminate this change. With this aim, we provide such an indicator based on patent data. Using na...

The question of whether AI substitutes or complements human work is central to debates on the future of work. This paper examines the impact of AI on skill demand and compensation in the U.S. economy, analysing 12 million online job vacancies from 2018 to 2023. It investigates internal effects (within-job substitution and complementation) and external effects (across occupations, industries, and regions). Our findings reveal a significant increase in demand for AI-complementa...

Artificial Intelligence (AI) will change human work by taking over specific job tasks, but there is a debate which tasks are susceptible to automation, and whether AI will augment or replace workers and affect wages. By combining data on job tasks with a measure of AI susceptibility, we show that more highly skilled workers are more susceptible to AI automation, and that analytical non-routine tasks are at risk to be impacted by AI. Moreover, we observe that wage growth premi...

This paper examines labor market polarization through a comparative analysis of skill-based employment and wage distributions in India and the United States during 2018-2023, with particular attention to differential automation risks and AI preparedness. Using detailed occupation-level data, automation risk metrics, and a series of statistical tests including wage premium analysis, employment share tests, and wage-employment regressions, we document significant structural dif...

Substantial scholarship has estimated the susceptibility of jobs to automation, but little has examined how job contents evolve in the information age as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs. Here we explore patterns and consequences of changes in occupational skill and characterize occupations and workers subject to the greatest re-skilling pressure. Recent work found that changing skill requirements are greatest...