Automation

Model legal regulation of AI in the public sector

This impact-driven project will produce concrete solutions (including model legislation) to regulate the use of AI by government agencies and public officials. The set of technologies which are popularly described as AI have radically augmented human capacities for virtuous and vicious behaviour. The widespread adoption of AI in the public sector has the potential to fundamentally alter the relationship between citizen and government.

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Algorithm as fact: Judicial review of the transparency of algorithms in government decision-making

This project builds on the lessons from Robodebt to address the need to understand the working, and impact, of automated processes in government. The problem with robo-debt lay not in the idea, but in the ¬execution. The system was riddled with design flaws. Robodebt is a textbook example of how not to deploy technology in government decision-making. An initiative designed to save revenue has instead led the federal government to repay $721m to 373,000 people. However, care must be taken not to draw the wrong lessons¬.

Rule as code: The use of machine learning as delegated legislation

Automation promises major benefits to government. Well-designed systems can make government services more accur¬ate, efficient and fair, such as by limiting the potential for human bias and error. They can also ¬extend taxpayer dollars so that services can reach many more people. An example is in legal services, where government funding of legal aid and community legal centres is critically important to helping thousands of vulnerable people, yet still falls well short of what is needed.

Humanising Machine Intelligence Project

The Humanising Machine Intelligence project brings together philosophers, social scientists, legal scholars and computer science experts to design AI that represents and promotes "Australian values".

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Updated:  10 August 2015/Responsible Officer:  College General Manager, ANU College of Law/Page Contact:  Law Marketing Team