570100 Data Ethics and Regulation

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.

Description

This subject focuses on the regulation and ethics of data practices in the digital environment. Students gain a deeper appreciation of the moral and ethical foundations of privacy, security and accountability and apply them to topics such as the ethics and regulation of data collection activities, algorithmic accountability and the biases inherent in data analytic tools.

Subject learning objectives (SLOs)

Course intended learning outcomes (CILOs)

This subject engages with the following Course Intended Learning Outcomes (CILOs), which are tailored to the Graduate Attributes set for all graduates of the Faculty of Arts and Social Sciences:

• Evaluate the assumptions implicit in data and apply analytical techniques to create innovative business solution (1.1)
• Communicate complex data-informed decisions clearly in written, verbal and visual form to a range of business stakeholders (2.1)
• Critically evaluate data practices and use these insights to improve analytical processes which drive positive and equitable outcomes (3.1)
• Integrate advanced data analysis techniques with business practices to deliver new insights that drive effective decision-making in local and international contexts (4.1)

Contribution to the development of graduate attributes

This subject engages with the following Program Intended Learning Outcomes (PLOs) which are tailored to the Graduate Attributes set for all graduates of the UTS Business School’s Master of Business Analytics:

• Convey information clearly and fluently in written and verbal form appropriate for the problem, data and stakeholders (3.1)
• Interact with colleagues and stakeholders to work effectively towards agreed outcomes (3.2). This PLO is met in class activities related to the fifth criterion in Assessment 1: Clarity and creativity of the infographic and relevance to the content of the article. Peer collaboration takes place to inform development of individually submitted work.
• Demonstrate understanding of principles of sustainability, ethical and social responsibility as well as Indigenous values relating to professional practice in data analytics (4.1)
• Demonstrate technical and adaptive skills in data analytics relevant to business contexts (5.1)

Teaching and learning strategies

This unit is made up of three modules delivered online over six weeks. Students work through each 2-week module at their own pace and momentum is maintained through weekly interactive activity attached to each theme and/or concept within the modules. Over the six weeks there will be three synchronous one hour online synchronous interactive sessions that discuss the module, and provide opportunities for task-based group activity, discussion and Q & A sessions (broken up into 15min segments).

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 an online UTS site (Canvas’) ‘essay’ feature. These interactive elements form the basis of weekly online discussions which are moderated by teaching staff over the six weeks.

Content (topics)

Module 1: Key concepts. In the first module students are introduced to key concepts and definitions across the two main areas of the subject. Module one covers two key questions: (1) What is data, why is it valuable and how is “datafication” related to processes that turn data into information, knowledge and economic value. This part of the module encourages students to build a more nuanced understanding of what different types of data there are, what different forms they can take and what they can and can’t do for organisations, institutions, society and individuals; (2) What ethical precepts guide the way we might think about good data stewardship? Here students are introduced to some simple interpretations of different Western ethical traditions (utilitarianism, social contract, deontology) and make sense of them in light of current data-driven practices, problems and opportunities. This insight is then used to interpret and unpack various ethical and regulatory frameworks covered in the second module.

Module 2: Frameworks. In the second module, students are introduced to a number of legal and regulatory frameworks that govern contemporary data stewardship practice. The second module casts a wide net across the regulatory landscape covering areas including consumer rights, health, geospatial data standards, intellectual property and licencing of datasets, metadata/quality standards, Freedom of Information (FOI), open data and the data sovereignty of indigenous and minority groups. Students also examine the role of different regulatory bodies and the channels of recourse available to both consumers and institutions when there are instances of data harms or malpractice.

Module 3: Case Studies. In the third module, students are presented with a list of case studies from which they can choose and explore areas/industries/problems based on their own interests. This provides a more individualised way of consolidating learnings from modules one and two

Assessment

Assessment task 1: Communicating Concepts in Data Ethics

Assessment task 2: Response to Hypothetical Scenario

Minimum requirements

No minimum requirements