57304 The Ethics of Data and AI
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 570100 Data Ethics and Regulation
Description
Data-driven AI systems form a key component of decision making in politics, culture, industry, and everyday life. Consequently, we are now seeing them at the centre of negotiations among different interests. The relationship between humans, data, and intelligent systems involves ethical dilemmas that reinforce existing power dynamics and create new ones. This subject focuses on developing common ground for debates on the function and development of data-driven AI systems. Drawing on insights from the humanities and social sciences, students gain a deeper appreciation for how the use of AI problematises issues of privacy, security, risk, bias, and accountability through the study of a range of real and hypothetical examples and case studies. Students gain a deeper appreciation of the moral and ethical foundations of transparency, fairness, accountability, privacy, security, and algorithmic bias. These concepts are applied to topics such as the ethics and regulation of data collection activities, algorithmic accountability, and the biases inherent in algorithms and data analytic tools.
Subject learning objectives (SLOs)
| a. | Distinguish between the characteristics and significance of ethical challenges of data-driven and automated intelligent systems |
|---|---|
| b. | Analyse the ethical considerations that have arisen from the wide-scale collection and processing of data from and about individuals and social groups |
| c. | Compare national and international approaches to regulating and governing data-driven technology |
| d. | Apply knowledge of data ethics to understand the impact of Artificial Intelligence on organisations, individuals, society, and our future |
| e. | Demonstrate how data driven automated intelligent technologies affect Indigenous communities and develop knowledge on data harm minimisation and inclusive design as it specifically relates to Indigenous individuals, communities, and groups |
Teaching and learning strategies
This subject is made up of three modules delivered on campus and online over 12 weeks.
The modules in this subject are designed to introduce key ethical, and regulatory concepts and practices in the fields of data analytics and artificial intelligence. Real and hypothetical examples scenarios are provided to demonstrate how these concepts are applied and both the positive and negative social impact that can result from the incorporation or the lack of ethical data practices.
Within each online module, content will be delivered through a mixture of short video presentations, interactive worksheets, quizzes/ questionnaires and short summary/comprehension/annotation exercises for selected readings and concepts using the online ‘essay’ feature in Canvas. These interactive elements, which incorporate formative feedback, form the basis of weekly tutorial discussions which are moderated by teaching staff over the 12 weeks.
Tutorials involve group reflection on case studies to allow students to evaluate the design of data and artificial intelligence systems for their ethical vulnerabilities and the potential impact of these. Students will apply regulatory frameworks to enhance system design to be more socially appropriate.
Content (topics)
This subject consists of three linked modules. The first deals with key definitions and concepts relating to data-driven and AI technology and their outcomes as a matter of ethical concern. The second covers the current legal and regulatory environment surrounding this emerging class of technology and the third looks toward real and hypothetical examples of malpractice, discrimination and harm in the implementation of AI systems.
Module 1: Key concepts and definitions
- What is this “thing” called data?
- How algorithms are shaping our world
- What types of data are there and how are they different?
- Data interests and data cultures
- What are the different types of AI systems and what are their applications?
- Ethical theories that guide our thinking about automated data technologies?
- What problems do AI systems cause for those subjected to them (including sections on Indigenous data sovereignty, bias, privacy, consumer rights and cyber security)
Module 2: Frameworks and Codes
- What legal, regulatory, and ethical frameworks guide the development of automated data-driven technologies and systems
- How well do these frameworks cope with sociotechnical change?
- What are big technology companies doing about data ethics?
- Practitioner perspectives
Module 3: Case Studies
- Introduction to case study methodology
- Real-world case studies
- Hypotheticals
Assessment
Assessment task 1: Quiz
| Objective(s): | a, b, d and e | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Weight: | 20% | ||||||||||||||||
| Length: | 30 min | ||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Assessment task 2: Essay
| Objective(s): | a, b, c and d | ||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Weight: | 30% | ||||||||||||||||||||||||||||
| Length: | 1500 words | ||||||||||||||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Assessment task 3: Case Study
| Objective(s): | a, b, c, d and e | ||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Weight: | 50% | ||||||||||||||||||||||||||||||||
| Length: | 2000 words | ||||||||||||||||||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Required texts
No required texts, all materials made accessible to students online. See below for an indicative list.
Recommended texts
Broad, E. (2018, Jan 25) Australia, we urgently need to talk about AI The Ethics Centre
Broad, E. (2018). Made by humans : The AI condition. Melbourne University Publishing.
Klein, L., & D'Ignazio, C. (2024, June). Data Feminism for AI. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 100-112).
Lessig, L. (2006). Code : version 2.0 ([2nd ed.]). Basic Books.
McCorduck, P., & Cfe, C. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. AK Peters/CRC Press.
Novelli, C., Casolari, F., Rotolo, A., Taddeo, M., & Floridi, L. (2023). Taking AI risks seriously: a new assessment model for the AI Act. AI & Society, 1-5.?
Hasselbalch, G. (2021). Data ethics of power: a human approach in the big data and AI era. Edward Elgar Publishing.
Wajcman, J., & Young, E. (2023). Feminism Confronts AI. Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines, 47.
+ a comprehensive list of readings on Canvas