Governing Health AI for Social Licence and Equity: Comparative Pathways in Australia and India

Project description

This project investigates pathways through which social licence for AI-enabled health technologies may be developed and sustained at population scale, with a focus on access and equity. Social licence refers to the ongoing acceptance and approval by patients, clinicians, policymakers, and the wider community, beyond legal compliance. It rests on perceptions of legitimacy, fairness, and trust. For AI in health—decision support, patient-facing tools, and ambient clinical documentation (e.g., AI scribe solutions)—effective governance to earn social licence is as critical as technical safety, especially for communities historically underserved. The project compares Australia and India to examine how different institutional settings and reform trajectories shape societal expectations and outcomes. Australia’s contested e-health records and privacy reform highlight transparency, consent, and accountability challenges, including for rural and First Nations communities. India’s rapid digital health expansion alongside new data-protection law shows the challenge of building trust at scale across multilingual, resource-constrained contexts. Parallel study will identify governance principles that can travel across settings, and where tailoring is required. The aims are to map societal narratives that confer or erode social licence for AI in health; identify policy and organisational conditions under which approval is maintained or withdrawn; and produce practical guidance to strengthen trust, access, and equity. A mixed-methods design will be used. Large-scale social media analysis will trace how narratives of trust, concern, and fairness emerge and diffuse. Comparative analysis of policy and governance frameworks will locate points of convergence and divergence. Targeted interviews and focus groups in each country will probe expectations, trade-offs, and acceptable safeguards. The findings will deliver evidence-based governance frameworks, monitoring indicators, and policy guidance to support responsible and legitimate adoption of AI in health across Australia and India. They will equip decision-makers with pathways to build and sustain social licence while improving access and equity.

Outcomes

1. Narrative Mapping Report: Empirical analysis of social media and public discourse on trust, legitimacy, and fairness in health AI across Australia and India. 2. Comparative Governance Analysis: Examination of policy and institutional arrangements in both countries, identifying convergences, divergences, and implications for social licence and equity. 3. Equity and Access Framework: Development of a conceptual framework and preliminary indicators to assess how AI adoption affects diverse and underserved populations. 4. Stakeholder Perspectives Report: Insights from interviews and focus groups with patients, clinicians, policymakers, and community representatives on conditions under which social licence is granted, sustained, or withdrawn. 5. Principles for Governing Social Licence: Evidence-based principles derived from the findings to guide how health systems can strengthen legitimacy, fairness, and trust in AI adoption. 6. Policy-Relevant Insights: Context-sensitive insights for policymakers and health organisations to consider when embedding equity and social licence into AI governance. 7. Cross-Country Case Illustrations: Comparative cases from Australia and India highlighting key challenges, strategies, and lessons for governing health AI. 8. Conceptual Monitoring Approach: Proposal of conceptual tools and ideas for tracking how social licence and equity evolve over time, to inform future applied research. 9. Academic Publications: Peer-reviewed articles and conference presentations disseminating theoretical and empirical contributions on governing health AI. 10. Researcher and Network Contributions: Active contribution to academic and practitioner networks in Australia and India, supporting ongoing dialogue on health AI governance.

Essential capabilities

Critical thinking and analytical ability, high level of written and oral communication in English

Desireable capabilities

Some research experience, knowledge and skills in qualitative research methods and text analytics

Expected qualifications (Course/Degrees etc.)

Master’s degree in health and behavioural sciences, social sciences, humanities, computer science, business, management, or related fields.

Additional information for applicants

note: i-students must have own scholarship to apply (CSIR, UCG-NET, etc)