ADM+S draws on the humanities, social and technological sciences. Our research is designed to expand, integrate, and apply our knowledge of the dynamics of ADM. In order to achieve this, we combine four research programs and four focus areas.
Select the yellow items in our interactive diagram below to explore our research areas.
PARTNERS
OUR VISION
FOCUS AREAS
Data science +
Critical data studies +
Computer science +
Law +
Regulatory theory +
Economics +
Ethics +
Sociology +
Design +
Anthropology
Ethical design for ADM machines +
Sustainable governance +
Public engagement,
co-design and trust
THE RESEARCH PROGRAMS
Automated decision-making systems are not purely technological: they comprise a number of elements, including the data that enables predictive decision-making machines; the institutions that provide regulatory settings; and the people who design, use, imagine and adapt automated systems, and whose lives are affected by them.
The Centre’s four Research Programs — Data, Machines, Institutions, and People — examine all the different elements that constitute automated decision-making systems. Together, they enable us to build the first comprehensive account of the social development, distribution, dynamics, performance and effects of ADM.
THE FOCUS AREAS
The Centre’s four cross-centre Focus Areas bring together our researchers to look at how automated decision-making works in four key domains – News and Media, Transport and Mobility, Health, and Social Services.
Our focus areas have been chosen because:
- they are well-documented areas where decision-making technologies are widely used;
- they align closely to national research priorities and agendas; and
- they are domains where governments and communities have coordinated, regulated and pooled resources to manage and mitigate major social risks – to public health and welfare, mobility, and democracy.
The introduction of new, untested decision-making technologies in these domains creates new possibilities for success and failure in large-scale institutional systems that have been specifically designed to reduce individual and social hazards.
The Focus Areas provide material for many of the empirical investigations in the four research programs, and they ensure our research is directed towards engagement, translation and outcomes in exemplary and essential sectors. Their hallmark is close collaboration with our partner organisations and stakeholders.
GET INVOLVED
Would you like to be a part of our ground breaking research?
You can become a citizen scientist by participating in some of our key projects.
When you get involved in our research, you contribute to creating the knowledge and strategies necessary for responsible, ethical, and inclusive ADM.
RESEARCH PROJECTS
Identifying the opportunities, enablers and barriers for public interest litigation to promote accountability and fairness in automated decision-making.
Examining the political economy of ‘sex tech’ in order to identify how sexual technologies are being governed at scale, how sexual data is being collected, stored, shared and monetised, and how the material benefits of sex tech may be more equitably distributed.
Indentifying how misunderstandings of harm and safety flow into flawed data logics and ineffective automated digital platform responses.
Evaluating the moderation of social media content, which has become radically more reliant on machine learning classifiers during the Covid-19 pandemic.
What shapes the environmental impacts of data centres cooling infrastructures?
Creating a replicable framework for building capacity (expertise, literacy, data partnerships and data governance) to unlock the social value and impact of advanced data analytics, AI and ADM across the not-for-profit sector.
Maping the extent, qualities and diversity of conversations about automation and ADM in Australia and globally, using key social media data sources including Twitter and Facebook to collect posts and a wide range of media articles.
Operationalising new data partnerships and implementing data analysis to improve non-profit and humanitarian sector work.
Unpacking the biases in models that may come from the underlying data, or biases in software that could be designed with a specific purpose and angle from the developers’ point-of-view