This course introduces students to the methods, applications and governance of artificial intelligence (AI) and machine learning as they relate to public policy and economic analysis. The aim is to build the conceptual fluency and critical judgment required to read, evaluate and responsibly use AI- and machine learning-generated evidence in policy analysis and research.
The course is organised around three themes. The first introduces the foundations of machine learning, including the principles of learning from data, the nature of training and evaluation, and the key concepts that distinguish AI from machine learning, generative methods, and automation. The second examines the main families of methods used to generate evidence for policy analysis: methods for analysing text and language, methods for analysing image and visual data, methods for prediction and classification using structured administrative data, and methods that combine machine learning with causal inference for program evaluation and impact analysis. The course covers what the methods can and cannot reliably show, the assumptions they rely on, and the analytical risks they carry. The third theme addresses the ethics, governance and responsible use of AI in government. Topics include bias and fairness, transparency, accountability, privacy, and contestability, drawing on Australian and international cases.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Demonstrate understanding of the foundations of machine learning, the evaluation of model performance, and the application of core methods to policy analysis.
- Critically evaluate evidence generated by machine learning, identify sources of bias and error, and assess suitability for policy analysis.
- Apply machine learning to policy- and economics-relevant questions, and articulate clearly what these methods can and cannot reliably show.
- Analyse the ethical, governance, and societal implications of use of artificial intelligence in government, drawing on relevant cases and regulatory frameworks.
- Communicate artificial intelligence and machine learning-related analysis and advice clearly and effectively to technical and non-technical audiences.
Indicative Assessment
- Quizzes (20) [LO 1,2,3,4]
- Assignments (40) [LO 1,2,3,5]
- Final exam (40) [LO 1,2,3,4,5]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
The standard workload for a 6 unit course is 130 hours including class time and independent study.
Prescribed Texts
None.
Preliminary Reading
Chernozhukov, V., Hansen, C., Kallus, N., Spindler, M., and Syrgkanis, V. (2026). Applied Causal Inference Powered by ML and AI. arXiv:2403.02467.
Grimmer, J., Roberts, M. E., and Stewart, B. M. (2022). Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton University Press.
Athey, S., and Imbens, G. W. (2019). "Machine learning methods that economists should know about." Annual Review of Economics, 11, 685–725.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). "A survey on bias and fairness in machine learning." ACM Computing Surveys, 54(6).
Selected reports from the Productivity Commission, the Department of Industry Science and Resources, the OECD, and the European Commission's high-level expert group on AI.
Assumed Knowledge
Familiarity with quantitative reasoning is helpful but not required.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 14
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Offerings, Dates and Class Summary Links
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