Unit rationale, description and aim
AI is fundamentally transforming how organizations design, analyze, and implement decisions in complex, data-driven environments.
This unit provides an advanced exploration of AI-driven decision intelligence, integrating machine learning, optimizations, probabilistic reasoning, and systems thinking to improve decision quality under uncertainty. Students examine how AI models interact with human judgment, organizational processes, and real-world constraints, and how decision pipelines are designed, validated, and continuously monitored.
A major emphasis of the unit is the practical application of Responsible AI (RAI). Students develop capability in error and bias analysis, interpretability and explanation methods, counterfactual reasoning, data quality assessment, and robust testing. These skills enable students to identify model limitations, evaluate behavioral edge cases, and ensure algorithmic decisions remain transparent, contestable, and fair. Structured human evaluation is highlighted as an essential governance mechanism, demonstrating how human oversight complements automation to maintain safety, compliance, and ethical alignment.
The unit advances ACU’s mission of ethical innovation and supports the UN Sustainable Development Goals (SDG 9 and SDG 11).
The aim of this unit is to enable students, through modelling, simulation, critical analysis, and applied experimentation, to design AI-enabled decision systems that balance technical innovation with risk mitigation and social responsibility.
Campus offering
No unit offerings are currently available for this unit.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 analyse principles and frameworks of de...
Learning Outcome 01
Design and implement AI-driven decision-support sy...
Learning Outcome 02
Evaluate the effectiveness and limitations of AI-b...
Learning Outcome 03
Communicate insights and recommendations from AI d...
Learning Outcome 04
Reflect on responsible innovation, governance, and...
Learning Outcome 05
Content
Topics will include:
- Introduction to Decision Intelligence and AI-enabled Decision-Making
- Decision Theory, Systems Thinking, and Causal Models
- Predictive Analytics and Reinforcement Learning for Decision Support
- Intelligent Automation and Workflow Optimisation
- Human-AI Collaboration and Hybrid Decision Systems
- Emerging Technologies – Quantum AI, Edge AI, and Autonomous Agents
- Governance, Fairness, and Accountability in AI Decision Systems
- Applied Project Presentations and Reflection
Assessment strategy and rationale
The assessment strategy is designed to build advanced analytical, technical, and ethical capabilities for applying artificial intelligence in decision-making and emerging technology contexts. All assessments are completed individually to ensure authenticity, independence, and clear evidence of personal mastery across conceptual, practical, and reflective domains.
The first assessment establishes the analytical foundation of the unit by requiring students to critically evaluate decision-intelligence theories, models, and frameworks, and examine their integration with contemporary AI techniques. This enables students to develop a rigorous understanding of how decisions are structured, supported, and governed in AI-enabled environments.
The second assessment translates this knowledge into applied practice. Students design, implement, and evaluate an AI-based decision-support solution using an emerging technology of their choice. This task assesses technical competency, modelling capability, and the ability to operationalise responsible AI principles within a functional decision pipeline.
The final reflective assessment enables students to synthesise their learning, assess ethical, cultural, and governance implications, and articulate professional insights on responsible innovation. This encourages accountability, critical reflection, and values-aligned judgment suitable for high-stakes AI contexts.
Together, these assessments provide a structured progression from critical understanding to applied implementation to ethical and professional reflection. This sequence supports the development of independent, responsible, and practice-ready graduates. There are no hurdle tasks in this unit.
To pass the unit, students must achieve all learning outcomes and an overall grade of 50% or higher.
Overview of assessments
Assessment Task 1: Analytical Report – Decision I...
Assessment Task 1: Analytical Report – Decision Intelligence Landscape
Students will individually analyse key frameworks and models of decision-making intelligence, examining their integration with AI technologies and ethical considerations in industry or societal contexts.
30%
Assessment Task 2: Applied Technical Notebook- AI...
Assessment Task 2: Applied Technical Notebook- AI Decision Mode
Students will individually design, implement, and evaluate an AI-based decision-support model using emerging technology (e.g., generative AI, IoT, blockchain). The submission includes annotated code, evaluation results, and interpretive discussion.
40%
Assessment Task 3: Reflective Essay – Responsible...
Assessment Task 3: Reflective Essay – Responsible Decision Intelligence
Students will individually produce a professional reflective essay discussing the implications of responsible AI, governance, and societal impact in decision intelligence, supported by real-world or case-based examples.
30%
Learning and teaching strategy and rationale
Learning and teaching in this unit adopt a research-informed and practice-oriented approach, designed to develop both conceptual understanding and applied capability in AI-driven decision intelligence. The fully online asynchronous structure provides flexibility while maintaining academic rigour through scaffolded, interactive activities.
Students engage with multimedia online sessions, curated readings, guided simulations, and analytical exercises that connect theory to practice. Learning activities are sequenced to promote critical reasoning, ethical reflection, and applied problem-solving in the context of decision intelligence systems. This approach supports independent inquiry and professional growth, enabling students to critically evaluate, design, and interpret AI systems that integrate human evaluation, ethical frameworks, and data-driven insights.
Representative texts and references
Representative Texts and Resources
Core Texts:
- Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
- Power, D. J. (2023). Decision Support, Analytics, and Decision Intelligence. Business Expert Press.
- Provost, F., & Fawcett, T. (2013). Data Science for Business. O’Reilly Media.
- Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
- Shrestha, Y. R., & von Krogh, G. (2022). Organizational Decision-Making with Artificial Intelligence: The Role of Human–AI Collaboration and Organizational Capabilities. MIS Quarterly Executive, 21(2), 143–157.
- Silver, D. et al. (2017). Mastering the Game of Go without Human Knowledge. Nature, 550(7676), 354–359.
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
- UNESCO. (2023). Ethics of AI for Governance and Decision-Making.
AI and Decision Modelling
- TensorFlow Decision Forests – https://www.tensorflow.org/decision_forests
- Reinforcement Learning Toolbox (MATLAB) – https://au.mathworks.com/products/reinforcement-learning.html
- Scikit-learn – https://scikit-learn.org/stable/
Analytics and Visualisation
- Azure Machine Learning – https://azure.microsoft.com/en-us/products/machine-learning
- Google Vertex AI Decision Intelligence – https://cloud.google.com/vertex-ai
- Power BI – https://scikit-learn.org/stable/
Responsible AI Resources
- AI Fairness 360 (IBM Research) – https://aif360.mybluemix.net/
- Explainable AI (XAI) Libraries (Google Cloud) – https://cloud.google.com/explainable-ai
- Introduction to the Quantum Programming Language Q - Azure Quantum | Microsoft Learn Microsoft Quantum | Homepage https://youtu.be/wSHmygPQukQ
- Responsible AI Toolkit (Microsoft) – https://www.microsoft.com/en-us/ai/responsible-ai