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Automated Decision-Making Empirical Mapping Project

Focus Area(s): News & Media, Social Services, Health, Transport & Mobilities
Research Program: Institutions
Status: Active

This project explores the complex interplay between automated decision-making (ADM) and generative artificial intelligence (GAI) technologies and the Australian labour market, utilising key variables such as geography, industry, sector, and occupation among others. The main objectives involve the development of a theoretical categorisation of AI systems, followed by its empirical application using statistical data from Australia (e.g. ABS labour and business surveys). Our goal is to map the influences of AI technologies across the economy, taking into consideration machine exposure (i.e., via efficiency, competency, or autonomy), human insulation (i.e., ability advantages), and institutional barriers (i.e., regulatory risks). 

Key outputs include an AI system taxonomy, a multifaceted scoring system for evaluating the interplay between machine and human tasks, and a database monitoring potential AI adoption and impact across various sectors. These are used for granular analysis of the potential risks and advantages associated with AI integration, identifying areas of high complementarity between technology tools and workers and areas of high susceptibility to machine substitution. 

Project outcomes provide a nuanced understanding of AI’s impact on the Australian labour market, establishing a predictive framework for future work dynamics. Our findings contribute significantly to businesses, academic research, and policy development by generating a detailed impact map of AI across industries. These insights could inform strategic actions, optimising AI benefits and mitigating risks, and shaping workforce development initiatives. Additionally, this project contributes to the broader discourse on AI’s ethical and societal implications by advocating a balanced approach to AI integration in the Australian labour market, thereby promoting harmonious human-machine coexistence and laying the groundwork for a prosperous, AI-enhanced future. 


Large Language Models Reduce Agency Costs, 2023

Ilyushina, N., Potts, J., et al.

Journal article

Profiting from data commons – Theory, evidence and strategy implications, 2023

Potts, J., et al.

Journal article

Decentralised autonomous organisations: A new research agenda for labour economics, 2022

Ilyushina, N., MacDonald, T.

Journal article


ADM+S Chief Investigator Jason Potts

Prof Jason Potts

Lead Investigator,
RMIT University

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Paul Henman

Prof Paul Henman

Chief Investigator,
University of Queensland

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Distinguished Professor Julian Thomas

Prof Julian Thomas

Chief Investigator,
RMIT University

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ADM+S Investigator Ivana Jurko

Ivana Jurko

Partner Investigator,
Red Cross Australia

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ADM+S Chief Investigator Megan Richardson

Prof Megan Richardson

University of Melbourne

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Australian Red Cross

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