Generative AI and the Workforce: What Are the Risks?

29 Pages Posted: 3 Oct 2023

See all articles by Emmanuelle Walkowiak

Emmanuelle Walkowiak

RMIT

Trent MacDonald

RMIT University; RMIT Blockchain Innovation Hub; ARC Centre of Excellence for Automated Decision-Making and Society

Date Written: September 12, 2023

Abstract

The unprecedented and rapid diffusion of generative AI represents a major disruption for the labour market, bringing new opportunities and risks within the same transformation. Our paper addresses two significant questions: What are the occupations exposed to generative AI in Australia? What are the risks associated with the diffusion of generative AI within occupations?

This paper introduces a novel methodology to quantify, map, and analyse workers’ exposure to generative AI (GenAI) risks with a task-based approach. By combining the Australian Labour Force Survey, the Australian Skills Classification, and synthetic data, we provide the first evidence-based analysis of the occupational context of risks associated with workers’ use of generative AI.

The exposure of the Australian workforce to GenAI is substantial with 80% of workers allocating 20% of their time to tasks exposed to Large Language Models (LLMs). LLM use also changes the quality of jobs by exposing workers to new risks, including potential privacy risks in 12% of tasks, cybersecurity in 14%, professional standards breaches in 14%, unethical bias in 14%, misinformation and manipulation in 11%, safety and physical harm in 26%, liability and accountability in 26%, and intellectual property risks in 10%. These mapping demonstrates large potential for productivity gains, for job displacements, and for the transformation of the quality of jobs that could occur with the diffusion of GenAI. 

Keywords: Generative AI, AI risks, Australian labour market, Synthetic data

JEL Classification: J24, O33, O56

Suggested Citation

Walkowiak, Emmanuelle and MacDonald, Trent, Generative AI and the Workforce: What Are the Risks? (September 12, 2023). Available at SSRN: https://ssrn.com/abstract=4568684 or http://dx.doi.org/10.2139/ssrn.4568684

Emmanuelle Walkowiak (Contact Author)

RMIT ( email )

Melbourne
Melbourne
Australia

Trent MacDonald

RMIT University ( email )

124 La Trobe Street
Melbourne, VIC 3000
Australia

RMIT Blockchain Innovation Hub ( email )

106-108 Victoria Street
Carlton, VIC 3053
Australia

ARC Centre of Excellence for Automated Decision-Making and Society ( email )

106-108 Victoria Street
Carlton, VIC 3053
Australia

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