FIT1066 · Responsible use of data in the age of AI

Overview

This unit explores the critical role of data in shaping AI systems, from how data is collected and governed to how it is transformed and interpreted to support analytical and machine‑learning workflows. You will examine what data is, where it comes from, and how technical, organisational, ethical and societal factors influence its quality and suitability for AI applications. Through practical activities, you will clean, reshape and validate datasets, working with real‑world data sources to prepare them for analysis and model development. You will investigate issues such as consent, bias, intellectual property, data sovereignty and environmental impacts, considering how these factors constrain responsible data use. Using AI‑enabled tools to support exploration and validation, you will build sound judgement about data integrity and its influence on downstream AI outcomes.

Offerings

Assessment

The Handbook publishes no assessment items for this unit yet.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Effectively manage the end-to-end data lifecycle for AI (collection, storage, access, governance, and use) for a given data source, including considerations of integrity, IP, and data sovereignty in line with responsible practice;
2. Ingest, clean and transform datasets to produce analysis‑ready data structures suitable for AI workflows;
3. Identify privacy and security risks in a selected data source and propose practical safeguards and storage choices that reduce exposure while enabling legitimate analysis and use;
4. Use AI‑assisted tools to examine potential ethical risks in datasets - such as bias, harmful patterns, or inappropriate data use - and document limitations, uncertainty and mitigation considerations transparently.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

Student experience, not official rules. Nothing here changes what the Handbook says.