Definition of Artificial Intelligence (AI)
AI-driven systems typically involve models that learn, adapt, or generalise from data over time. In contrast, rule-based automation relies on explicitly defined logic and does not learn from user behaviour. While both are often grouped under the “AI” label in industry, they represent fundamentally different system capabilities, with distinct implications for UX, risk, transparency, and control.
Designing AI-Infused Systems
Throughout my career, I have designed and delivered numerous complex, high-stakes systems built on deterministic, rule-based automation — particularly within financial and enterprise environments. While I have not yet worked on a production system powered by true learning-based AI, I bring strong, transferable UX and product design expertise grounded in complex decision-making, data-intensive workflows, and risk-aware system design.
Building Foundational Knowledge
To build formal grounding in AI-specific design considerations, I have undertaken structured study in Human–AI Systems Design through the University of Cambridge. This has equipped me with foundational knowledge of key challenges in AI-infused systems, including interpretability and explainability, human oversight and control, error and risk management, AI ethics, and governance — and how these considerations shape user trust, accountability, and interaction design.
HCI (Human Computer Interaction) for AI Systems Design from the University of Cambridge
Covering the design of innovative systems that blend automation, intuitive user experiences, ethics and risk management — to help shape the future of Human-AI Interaction.
Some key topics covered:
- Enabling Human-AI Interaction
- Function Modelling
- Function Structures
- Function Analysis Systems Technique (FAST) Diagrams
- Concept Generation & Evaluation
- Automation
- Mixed-Initiative Systems, Teaming & Partnerships
- Understanding and Interpreting AI
- Decision-making in a Dynamical System
- Heuristics and Cognitive Biases
- Interpretable AI for Decision-making
- Managing Control and Agency
- Measuring Agency
- Control
- Human-in-the-Loop
- Interactive Machine Learning and Machine Teaching
- Governance of Human-AI Systems
- Defining Risks and Hazards
- Hazard Identification and Risk Assessment Methods
- System Boundaries and Mapping
- Alignment
- Principles for Safe and Ethical AI Systems
- Evaluating Human-AI Systems
- Guidelines for Human-AI Interaction
- Deployment Studies
Final Scoring
Passed with a final project assignment score of 86.7%, with an overall final course score of 94%.
