AI and automated decision-making tools promise money and unmatched power to banks and governments alike: not only, so the saying goes, will they know everything about their citizens and customers, but will also be able to predict their behaviour, preferences and opinions. A global consulting firm McKinsey estimates that AI technologies will unlock 1 trillion dollars additional value for global banking industry every year. No wonder, governments around the world are quick to jump on the AI bandwagon, expecting increased efficiency, reduced costs and better insights into their populations. But will AI and automated decision-making meet these promises?
Many of us are researching these cutting-edge issues from different angles and starting points. Some researchers focus on AI-driven innovation in the industry or public sector. Some are passionate about justice and new harms that arise when business and governments automate their decisions. Others look at how AI is transforming wider structures of economy and governance. Yet, we rarely talk to each other.
Divided by artificial disciplinary distinctions of ‘public’ and ‘private’ law, we hardly appreciate that many automated decision-making and AI tools, which governments are eagerly applying today, have been developed and experimented with for decades in the private sector. For example, we do not often talk about how China’s Social Credit System has roots in automated credit scoring in the financial industry. Similarly, we do not discuss how data-enabled fraud detection, used in the Australian Robo-Debt system, has long been a common practice in the banking industry. India’s national ID system Aadhaar incorporates many different automated decision-making and AI tools, such as facial recognition and profiling, long experimented with in the private sector.
As these examples illustrate, technology tools, along with the broader managerial culture, are often transferred from private corporations to government departments. At the same time, governments are the ones funding the initial development of these tools, later to be commercialized by corporations. Corporate and trade secrecy means we simply don’t know how latest technology is used by the industry and what new tools are being developed at the moment. Yet, these tools will soon reach public administrations, and will be incorporated into ever larger Automated States, dealing with welfare, taxes and public money. Such close and mutually reinforcing relationship between the industry and public administration could teach us about the future possibilities and societal dangers of AI and automated decision-making in finance and public administration. It could also teach us about accountability, better regulation, and scrutiny.
In this conference, we want to promote dialogue between the ‘public’ and ‘private’ lawyers and scholars, technology critics, enthusiasts and pessimists about “Money, Power and AI”. Sharing our different perspectives can lead to a more holistic understanding of the shifts that are reshaping our financial and public institutions, our economies and societies.
Contributions to the conference include reflections on the following questions:.
Conference Format & Registration
COVID restrictions permitting, the conference will be held in-person and online, all times are in Australian Eastern Daylight Time (AEDT)
Both in-person and online registration is free.
Conference Papers & Edited Collection
Selected papers will be invited to be published in a post-conference edited collection, titled: “Money, Power and AI: From Automated Banks to Automated States” with around 10 contributions of 6.000-8.000 words. Full papers will need to be provided by 1st March 2022. When sending in your abstract please indicate whether you are interested in contributing to the edited collection, we will provide further details in due course.
Conference Hosts and Sponsors
In addition to the hosts and sponsors listed above, this conference is also partially funded by the Australian Research Council Discovery Early Career Research Award (‘Artificial Intelligence Decision-Making, Privacy and Discrimination Laws’, project number DE210101183) and by the Santander Financial institute (SANFI) award for Early Career Researchers (‘Regulating the Use of AI and Big Data in Retail Financial Services: Promoting Innovation and Preventing Consumer Harm’).
We acknowledge the Bedegal people of the Eora Nation that are the Traditional Custodians of this land in which UNSW is situated. We pay our respects to their Elders past, present and emerging.