FIT5218 Human-centric AI
Faculty of Information Technology
FIT5218 Human-centric AI is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2022. It needs FIT5047 and FIT5197.
- Credit points
- 6
- Offered in 2022
- Not offered
- Workload
- 12 hours
- per semester
The 2027 handbook has no page for FIT5218. This is its 2022 entry, the latest one.
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Requisites
Before FIT5218
Prerequisites
Pass these before you enrol.
After FIT5218
No unit lists FIT5218 as a prerequisite in the 2022 handbook.
Overview
This unit will explain how AI technologies are enabling more deeply human-centred design, including walking through illustrations of implementing predictive behavioural analytics and adaptive interface design in application domains like medicine and education. It will summarise the major design and development themes associated with implementing human-centred AI systems on current platforms, such as robotics, automotive, smartphones and wearables. Students will learn the philosophy, foundations, models, rationale and multidisciplinary origins of human-centred AI. Emphasis will be placed on students' critical analysis of AI technologies, based on an examination of their positive vs. negative impact on users and society. Students will hear from experts on the current ethical, legal, and regulatory challenges involved in developing pro-social AI systems, and guidelines for avoiding major pitfalls in these areas.
Offerings in 2022
The 2022 handbook lists no offerings for FIT5218.
Learning outcomes
When you finish this unit, you should be able to:
- 1
critique AI only systems, transform them into human-centred AI systems and identify the motivation and benefits for doing the transformation;
- 2
propose and complete a project that represents a human-centred multidisciplinary AI model;
- 3
utilise tools for signal processing and interpretation required for behavioural analytics (e.g. for predicting users' social/emotional, cognitive, or health/mental health status);
- 4
critically evaluate empirical findings on the positive versus negative impact of AI technologies on human users and society, and reuse this knowledge to hypothesise the impact of future AI technologies;
- 5
recognise ethical, legal and regulatory challenges associated with developing different types of AI applications.
Workload and teaching
Minimum total expected workload equals 12 hours per week comprising:
- Two hours/week lectures
- Two hours/week laboratories
A minimum of 8 hours per week of personal study for completing lab activities, assignments, private study and revision.