Unit rationale, description and aim
As artificial intelligence and emerging technologies transform global industries and societies, professionals must demonstrate not only technical mastery but also ethical judgment, cultural awareness, and leadership integrity. This unit prepares postgraduate students to lead responsibly in the governance and implementation of advanced digital innovations.
Students critically evaluate ethical theories, governance frameworks, and professional standards relevant to AI, data analytics, and automation. They explore how values, power, and culture shape technological design, policy, and impact, drawing on diverse perspectives including Aboriginal and Torres Strait Islander knowledges to promote inclusion and social responsibility.
Through research-informed inquiry, collaborative dialogue, and applied policy analysis, students engage with real-world case studies and professional dilemmas to build advanced capability in ethical leadership and decision-making. Learning activities encourage reflective practice, strategic thinking, and intercultural understanding across local and global contexts.
The aim of this unit is to develop ethical leaders and reflective practitioners who can integrate ethics, empathy, and professional responsibility into technology governance and innovation. Graduates will be equipped to influence policy, guide responsible design, and foster trust and fairness in digital transformation
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Critically evaluate and synthesise advanced ethica...
Learning Outcome 01
Integrate intercultural, social, and ethical persp...
Learning Outcome 02
Design and communicate ethical frameworks and prof...
Learning Outcome 03
Reflect critically on professional identity, leade...
Learning Outcome 04
Content
Topics will include:
- Ethics, Professionalism, and the Role of Leadership in Technology
- Advanced Ethical Theories and Global Decision-Making Frameworks
- Professional Codes and Standards in AI and Computing
- Law, Policy, and Governance in AI and Emerging Technologies
- Equity, Fairness, and Cultural Perspectives in Digital Innovation — including First Peoples’ knowledges and values
- Ethical Leadership and Decision-Making in Practice
- Intercultural Collaboration and Global Ethics
- Sustainability, Responsibility, and the Common Good informed by Australia’s First Peoples’ perspectives on stewardship and interconnectedness
- Governance, Risk, and Accountability in AI
- Policy, Advocacy, and Public Engagement in Emerging Technologies
- Professional Identity, Reflective Practice, and Lifelong Learning
- Future Directions: Ethics, Foresight, and Transformative Innovation
Assessment strategy and rationale
The assessment strategy for this unit is designed to progressively build and demonstrate advanced ethical reasoning, professional communication, and leadership capability in the context of AI and emerging technologies. Assessments are scaffolded to move from individual critical analysis to collaborative policy design, and finally to reflective professional identity development. Assessment 1 introduces foundational mastery by requiring students to critically evaluate ethical theories, professional standards, and governance frameworks. This establishes the theoretical and analytical base for applied ethical judgment. Assessment 2 extends learning through collaborative inquiry and policy development, where students design ethical and governance frameworks for real-world technological issues. This task strengthens teamwork, intercultural competence, and professional communication skills. Assessment 3 consolidates knowledge and leadership growth through a reflective professional portfolio, enabling students to evaluate how empathy, integrity, and ethical foresight inform their identity as responsible innovators.
Across all tasks, students engage with authentic case studies and research-informed practice, applying theoretical concepts to practical and policy-oriented contexts. This approach ensures alignment with the unit learning outcomes, promotes ethical leadership for the common good, and reflects the expectations of Master’s-level professional education.
To pass the unit, students must achieve all learning outcomes and an overall grade of 50% or higher.
Overview of assessments
Assessments are designed to develop advanced ethical judgment, professional leadership, and applied research skills through authentic, industry-relevant activities.
Assessment Task 1: Advanced Ethical Reasoning and...
Assessment Task 1: Advanced Ethical Reasoning and Analysis
Students critically evaluate ethical theories, governance frameworks, and professional standards to address complex issues in AI and emerging technologies.
30%
Assessment Task 2: Collaborative Policy Brief and...
Assessment Task 2: Collaborative Policy Brief and Presentation
Students design a responsible AI prototype, produce an ethical impact report, and deliver a professional presentation
40%
Assessment Task 3: Reflective Portfolio: Ethical ...
Assessment Task 3: Reflective Portfolio: Ethical Leadership and Professional Identity
Individual professional portfolio integrating ethical reflection, leadership development, and a strategic action plan for responsible innovation.
Students reflect on their leadership development, ethical growth, and capacity to apply empathy, integrity, and accountability in professional contexts.
30%
Learning and teaching strategy and rationale
This unit employs a research-informed, active, and reflective learning approach to develop students’ capability for ethical reasoning, professional judgment, and intercultural leadership in technology contexts. Learning activities are designed to move progressively from the critical analysis of ethical theories and governance frameworks to the application of ethical leadership and policy development in professional and organisational settings. Students engage through a blend of interactive online modules, case-based discussions, and collaborative projects that connect theoretical insight with real-world decision-making. Authentic case studies from AI, data analytics, and automation are used to strengthen students’ ability to analyse complex ethical issues and propose responsible, evidence-based solutions.
By combining critical inquiry, applied analysis, and reflective practice, students build the confidence to integrate ethics, empathy, and accountability into leadership and innovation across emerging technologies.
Representative texts and references
Akhundov, A. (2025). The role of ethics in modern technology development. Porta Universorum, 1(4), 169–177.
Association for Computing Machinery (ACM). (2018). ACM Code of Ethics and Professional Conduct. https://www.acm.org/code-of-ethics
Australian Computer Society (ACS). (2022). ACS code of professional conduct. https://www.acs.org.au/content/dam/acs/rules-and-regulations/ACS-Code-of-Professional-Conduct.pdf
Baase, S., & Henry, T. (2018). A gift of Fire: Social, legal, and ethical issues in computing (5th ed.). Pearson.
Boddington, P. (2017). Towards a Code of Ethics for Artificial Intelligence. Springer. https://doi.org/10.1007/978-3-319-60648-4
Brookshear, J. G., & Brylow, D. (2019). Computer Science: An overview (13th ed.). Pearson.
Chithra, N., & Bhambri, P. (2024). Ethics in sustainable technology. In Handbook of technological sustainability (pp. 245–256). CRC Press.
Farayola, O. A., & Olorunfemi, O. L. (2024). Ethical decision-making in IT governance: A review of models and frameworks. International Journal of Science and Research Archive, 11(2), 130–138.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4
Nisan, N., & Schocken, S. (2021). The Elements of Computing Systems (2nd ed.). MIT Press.
Reynolds, G. (2019). Ethics in Information Technology (6th ed.). Cengage Learning.
Ribeiro, D., & Varajão, J. (2025). Codes of Ethics and Conduct in Information Systems: Towards a unified framework. https://doi.org/10.1007/s11301-025-00521-9
Shapiro, H. (2020). Ethics, Technology, and Engineering: An introduction (2nd ed.). Cambridge University Press.
Vermaas, P., Ammon, S., & Mehnert, W. (2024). Toward a Code of Conduct for Technology Ethics Practitioners. https://doi.org/10.1080/23299460.2024.2440958
Standards and Guidelines
ACM Code of Ethics and Professional Conduct –https://www.acm.org/code-of-ethics
IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems – https://ethicsinaction.ieee.org
IFIP Code of Ethics and Professional Conduct – https://www.ifip.org