Automated Decision-Making Empirical Mapping Project

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.