The Social Services Focus Area will investigate ADM uses and implications in disability services, child protection, criminal justice and income support.
ADM is increasingly used in the design, delivery and governance of many social services by governments, private and not-for-profit organisations. Influential advocates (e.g. OECD 2017) argue for further automation to promote efficiencies, reduce human errors and bias, and enhance personalised service delivery, choice and resourcing.
Yet, ADM in social services is also highly problematic, with detractors arguing that professionally-trained human decision making is necessary in negotiating service provision to meet complex human circumstances and needs. There are also considerable ethical challenges relating to bias, discrimination and surveillance, particularly when used on disadvantaged populations which rely on social services.
RESEARCH PROJECTS
Public Interest Litigation for AI Accountability
Identifying the opportunities, enablers and barriers for public interest litigation to promote accountability and fairness in automated decision-making.
Ecological Implications of Data Centres
What shapes the environmental impacts of data centres cooling infrastructures?
Transparent Machines: From Unpacking Bias to Actionable Explainability
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
Governing ADM Use
Examining the challenges to, and opportunities for, liberal and democratic institutions and governance presented by ADM.
Democratic Practices of Governance Given ADM
Developing a theoretically rich analysis of democracy and freedom given ADM.
The Coronavirus Impact
Considering ethical approaches in the area of automated decision-making (ADM) and civic life with a focus on civic commitments and concerns.
Data mapping and ADM to advance humanitarian action and preparedness
Operationalising new data partnerships and implementing data analysis to improve non-profit and humanitarian sector work.
Everyday Data Cultures
Exploring the role of everyday data practices and literacies in automated decision-making.
Mapping ADM Across Sectors
Examining common themes with respect to the issues raised by the collection, storage, and use of data for ADM across object domains.
Diverse Experiences of ADM: Design, Curation and Use
Examining the ways in which automated decision-making (ADM) is being integrated into the lives of diverse and non-dominant communities across Australia.
Data Ethics, Rights, and Markets
Considering ethical approaches in the area of automated decision-making (ADM) and civic life with a focus on civic commitments and concerns.
Mapping ADM Machines in Australia and Asia-Pacific
Mapping where ADM is and how it is being used in social services beyond Europe and North America, and into the Asia-Pacific region.
Data capacity and collaboration for ADM in the community sector
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.
Civic Automated Decision-Making
Considering ethical approaches in the area of automated decision-making (ADM) and civic life with a focus on civic commitments and concerns.
Considerate and Accurate Multi-party Recommender Systems for Constrained Resources
This project will create a next generation recommender system that enables equitable allocation of constrained resources.
Building Ethical Machines in Social Services: Examining, Evaluating, Building Fairness and Explainability in ADM
Exploring what social service users and professionals in disability services, child protection, criminal justice and social security regard as fairness and explanation.
Decentering ADM: A Review of Automated Decision-Making in the Global South
This project is a review of the current state of ADM implementation, practices and visions in different regions in the Global South.