Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

Abstract

We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars. Managerial Summary: Although artificial intelligence (AI) promises to spur economic growth, there is widespread concern that it could displace workers, alter industry trajectories, and reshape organizations. Despite the interest in this area, we have limited ability to study the effects of AI on occupations, firms, industries, and geographies because of limited availability of data that measures exposure to AI. To address this limitation, we create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level (AIIE)

Artificial intelligence and employment

“…28 By contrast, AI has made the least progress in applications that affect physical and psychomotor abilities. 29 This is consistent with emerging evidence that AI is capable of performing cognitive, non-routine tasks (Lane and Saint-Martin, 2021 [6]). Note: The link between O*NET abilities and AI applications (a correlation matrix) is taken from Felten, Raj and Seamans (2019 [2]).…”

“…Flexibility of closure is the ability to identify or detect a known pattern (a figure, object, word, or sound) that is hidden in other distracting material. 29 Only one psychomotor ability has an intermediate score: rate control, which is the ability to time one's movements or the movement of a piece of equipment in anticipation of changes in the speed and/or direction of a moving object or scene.…”

Artificial Intelligence and Firm-Level Productivity

“…According to the authors, for example, "telemarketers", "cargo and freight agents", or "watch repairers" would be at a dramatically high risk of automation whereas "recreational therapists" or "nutritionists" would be on the opposite extreme (see also Arntz et al 2016). In a similar approach, Felten et al (2021) this strand of the literature might not be strictly identifying the direct effect of AI adoption on productivity. In fact, as noted by Raj and Seamans (2018), the physical nature of robots, which make them a tangible capital asset easy to measure and track, on top of the availability of public data (e.g., IRF data on industrial robotics), have retained the attention on this area of most of the existing empirical work.…”

Exposure of occupations to technologies of the fourth industrial revolution

“…In addition, even though AI technologies have certain capabilities (such as image recognition), this does not necessarily mean that solutions have already been invented which can help workers conduct their tasks. Accordingly, Felten et al (2021) describe their exposure score as being forward-looking.…”