26811 Data, Algorithms and Meaning

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Subject handbook information prior to 2025 is available in the Archives.

UTS: Business
Credit points: 3 cp

Subject level:

Postgraduate

Result type: Grade and marks

There are course requisites for this subject. See access conditions.

Description

This subject introduces candidates to key machine learning algorithms and their application in real-world settings. Participants develop an understanding of big data, how algorithms work, as well as the strengths and weaknesses of big data and algorithms in the context of specific applications. Since data science problems are infused with assumptions, often with ethical and legal implications, due attention is given to questioning the assumptions behind data and approaches used to analyse it. Candidates explore ethical implications in relation to the use of machine learning algorithms and develop options on how to navigate ethical issues.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1.	Appraise the role and impact of algorithm design for decision making
2.	Evaluate risk mitigation approaches regarding privacy and fairness
3.	Evaluate societal concerns and potential areas of backlash for the application of algorithmic solutions in specific industries

Contribution to the development of graduate attributes

This subject is designed to help students develop a suitable understanding of the role that data and algorithmic solutions have for business decision making by identifying both the benefits and challenges of AI adoption, introducing students to the emerging research field of ethical algorithms, and applying key concepts to real-life applications.

This subject contributes to the development of the following graduate attributes:

Critical thinking, creativity and analytical skills

Teaching and learning strategies

This subject is delivered through a mix of online learning, three live online webinars and online consultations. The subject features a mix of theoretical concepts and application in the contemporary context that is designed to apply evidence-based judgement, analytical and creative skills to solve complex business problems.

Students have access to online resources, and self-directed learning activities and are expected to study online content provided via the UTS learning management system. They are required to complete online learning activities, which will help identify knowledge gaps and inform discussions. They are required to engage in online discussions with their peers and academics. Webinars are designed to present the theory and practice of the subject’s content. Students are required to complete pre-work activities before attending webinars. Discussions focus on the application of concepts, techniques and methods.

A mandatory, on-line formative assessment provides students with further feedback to direct their self-study. Ongoing general and individual feedback will be provided throughout the subject via consultation sessions. A summative assessment provides feedback on students' comprehension and application of learning. Students also receive formal feedback on assessment tasks.

Content (topics)

Artificial Intelligence (AI), analytics and machine learning
AI adoption and trust
Algorithm design
Privacy and fairness

Assessment

Assessment task 1: Case Study Analysis (Individual)

Objective(s):	This addresses subject learning objective(s): 1, 2 and 3
Weight:	40%
Length:	Minimum 1,500 words
Criteria:	Level and depth of insight from analysis Application and understanding of key ethical considerations Relevance to practice and delivering organisational outcomes

Assessment task 2: Report (Individual)

Objective(s):	This addresses subject learning objective(s): 1, 2 and 3
Weight:	60%
Length:	2,000 words + tables and appendices
Criteria:	Application of key concepts to enterprises Understanding of key design and implementation aspects Demonstrating knowledge of ethical and professional decision-making Justification for concerns and identification of strategies to mitigate or overcome ethical issues Clarity and completeness of written report

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.

References

Roth, A., & Kearns, M. (2019). The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Oxford.

Miscellaneous (2019). HBR's 10 Must Reads on AI, Analytics, and the New Machine Age. Harvard Business Review Press.

Schneier, B. (2016). Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. W. W. Norton & Company

Ohm, P. (2010). Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization. UCLA Law Review 57.

Narayanan, A & Shmatikov, V. (2008). Robust De-anonymization of Large Sparse Datasets. IEEE Symposium on Security and Privacy

Bolukbasi, T., Chang, K., Zou, J., Saligrama, V., and Kalai, A. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. 30th Conference on Neural Information Processing Systems (2016)

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness Through Awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference

Ioannidis, J. (2005). Why Most Published Research Findings Are False. PLoS Medicine