This course equips students to critically assess and reflect on the use and development of data innovations based on analytics and artificial intelligence (AI) in public policy and their wider societal implications. Students will engage with global academic and policy debates to gain a critical understanding of the implications of different types of data analytics and AI (e.g., predictive analytics, machine learning, and generative AI tools). The course will address trade-offs between societal/economic benefits vs. harms and negative impacts, especially considering communities and people who are historically and structurally marginalised.

Additionally, students will learn about the types of policies and ethical and responsible data and AI practices, which can help address inequalities and promote social justice. The course focus on developing a critical data and AI literacy, offering skills to engage critically with the usage and implication of data-driven tools and artificial intelligence, which are increasingly important for a number of sectors, including government, policymakers, industry, policy/digital rights advocacy organisations, civil society and communities.

Classes to run in Semester 2 and delivered in 3 hourly blocks, once per week, over 9 consecutive weeks of time-tabled on campus teaching.

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Policy brief (report) (20%), aligned with ILOs c, d, e, f, g

Students will compile a concise brief with recommendations for policy based on group work in class (1200 words).

Written Assignment (80%), aligned with ILOs a, b, c, d, e, f, g

In the final individual written assignment, students will write: i) an essay that uses their acquired conceptual and analytical skills to analyse a related current policy issue; ii) a complementary reflective learning journal of their learning journey in seminars and group work. (3800 words).

This course aims to enable students to:

■ Develop a critical understanding of the implications of the use of data analytics and Artificial Intelligence in public policy for different people and places.

■ Promote responsible use of these technologies to address inequalities and advance social justice.

■ Develop conceptual and analytical skills to assess potential implications of justice issues emerging from biases and gaps in data and evidence for policy and decision making.

■ Conduct project-based work to investigate how a current policy issue is framed in data systems, and what are the potential implications for different people and places, including minoritised and marginalised groups. 

By the end of this course students will be able to:

a) Identify and assess the ways that data and AI tools are used for policymaking, public service innovation, and other policy goals.

b) Reflect on various issues of data/AI innovations in policy and society-such as bias, ethics, justice, fairness, accountability, sovereignty, public trust, and responsible design and use of data/AI.

c) Analyse the implications of biases and gaps in evidence and data and AI algorithms for different people and places, especially considering communities and people who are historically marginalised and how these tools may challenge or perpetuate inequalities, injustice, and exclusion.

d) Evaluate the benefits and harms of using data and AI for different people and places to inform policy decisions and/or delivery of public services, e.g., socially, economically, politically, culturally, and other aspects.

e) Assess the approach, role, and agency/resistance that a variety of actors play in shaping the ethical use and development of data and AI in public policy (e.g., policymakers, civil society, industry, third sector/activist organisations, and grassroot communities);

f) Synthesise literature and evidence on data/AI from wide-ranging disciplines (e.g., critical data/AI studies, media and communication, science and technologies, social and public policy) as well as "grey literature" (i.e., from government at local/regional/national/multi-national levels, third sector, and grass-root community and activist spaces);

g) Write and communicate concrete recommendations to policymakers (or relevant agencies/organisations) on how to use data/AI responsibly and ethically in a public policy context.