We are seeking submissions for the Data for Policy 2024 Conference on a Special Track chaired by HTI Director Technology, Professor Sally Cripps on Accelerating collective decision intelligence: AI and measuring what matters for policy making.
The application of data science and the use of AI systems in government should enable better decisions by politicians, policymakers, and communities themselves. However, the decision landscape in government is characterised by complexity, ambiguity, and the politics of policy formation processes. Far from popular belief, useful information and evidence are often disparate and scarce; data privacy and other human rights are paramount; the cost of a poor decision is high and often irreversible; there are many and varied stakeholders all with different priorities, and the benefits and costs of digital technologies have the potential to be inequitably distributed.
As a result of these characteristics many communities and governments around the world face decision paralysis, confounded by the exponential growth of data supplies, insufficient data analysis capabilities, and inconsistent use of evidence in policymaking.
To solve the complex problems facing governments around the world – including breaking entrenched cycles of disadvantage, environmental change and democratic resilience – need people from different disciplines to work together, using data science.
The framework will need to:
Identify and structure shared questions that matter most to inform the decision-making needs in policy design and implementation processes
Understand the decision-making systems and needs, informing collective decision points to increase value.
Distinguish between cause and effect - to prioritise policy design and identify what works and impacts outcomes.
Adaptively uncover what information is needed to answer the question at hand, using real-time experimental design and thus measuring what matters most.
Acknowledge and explain estimates of the uncertainty inherent in inferring these cause-and-effect relationships to reinforce robust and explainable policy decisions that build trust with the public.
Fuse together many disparate types of information – from lived experience, existing relevant data, expert opinion and bespoke data collection.
Co-create and co-design with impacted communities to ensure the interpretation and adoption of recommendations in ways that are meaningful to local culture and context.
Establish governance procedures that ensure AI systems are accurate, accountable, fair, and fit for purpose.
Submissions will need to address any government related issue and include at least two of the below research areas:
Methods for causal inference and estimation
Collective intelligence techniques for policy formulation
Translation of analytic outputs findings into policy and community practice
Governance systems and accountability for integration into public policy.
Read more on the Conference Tracks and Submissions. Submissions are due 27 November.