COMP3425 Data Mining Assignment 1 2026 | Australian National University (ANU)

COMP3425 Data Mining Assignment 1 

This assignment specification may be updated to reflect clarifications and modifications after it is first issued.

You are required to submit a single report in the form of a single PDF file with a file name that includes your University U-number ID. The first page must have a clearly identified title and author, with both name and university U-number, which may form a separate cover page. You may also attach supporting information as appendices in the same PDF file. Appendices will not be marked. 

This is a single-person assignment and must be completed on your own. You must use quality reference material and carefully reference via in-text citations, including material provided to you in the course. Any material that you quote must have the source clearly referenced. It is unacceptable to present any portion of another author's work as your own. Anyone found doing so will be penalised in marks. In addition, ANU plagiarism procedures apply. This course introduces fundamental concepts that could potentially be addressed by certain Generative AI tools (e.g., ChatGPT). Hence, the use of any Generative AI tools is not permitted in graded assessments within this course. 

You are strongly encouraged to start working on the assignment right away. You can submit as many times as you wish. Only the last submission at the due date will be assessed.

Task

The Australian Computer Society Code of Professional Conduct 2014 is expected to be applied by all Computing Professionals in Australia. It sets out six values but stresses the primacy of the public interest as the overriding value. In 2018, the Australian Government Office of the Australian Information Commissioner released the Guide to Data Analytics and the Australian Privacy Principles (APP). In 2022, UNESCO published the Recommendation on the Ethics of Artificial Intelligence (SHS/BIO/PI/2021/1) for voluntary application by Member States. The recommendation is broad in scope and far-reaching in implementation responsibilities over the whole AI system lifecycle. It includes a statement of values and 10 principles that should be respected by all actors in the AI lifecycle, including “data scientists, end-users, business enterprises, universities and public and private entities” (p. 10). These three documents are provided with this assignment specification.

In addition to those papers, you must also read Clarke R. (2018), “Guidelines for the Responsible Application of Data Analytics”, Computer Law & Security Review 34, 3 (Jul-Aug 2018), which is provided with this assignment specification and hereafter referred to as the Guidelines. You must also read the paper, Du, Liu and Hu (2020) “Techniques for Interpretable Machine Learning”, Communications of the ACM 63(1), which is also provided with the assignment.

You are to consider the application of the ACS code of conduct, the 10 UNESCO Principles, Clarke’s Guidelines and Du et al’s Techniques to the following fictitious insider detection scenario. You may also use the APP guide, which is helpful.

Insider Detection Scenario (from Clarke R. (2016) “Big Data, Big Risks”, Information Systems Journal 26, 1 (January 2016) 77-90, PrePrint at http://www.rogerclarke.com/EC/BDBR.html)

A government agency receives terse instructions from the government to get out ahead of the whistleblower menace, with Brutus, Judas Iscariot, Macbeth, Manning and Snowden invoked as examples of trusted insiders who turned. The agency increases the intrusiveness and frequency of employee vetting and lowers the threshold at which positive vetting is undertaken. To increase the pool of available information, the agency exercises its powers to gain access to border movements, credit history, court records, law enforcement agencies’ persons-of-interest lists, and financial tracking alerts. It applies big data analytics to a consolidated database comprising not only those sources, but also all internal communications, and all postings to social media gathered by a specialist external services corporation.

The primary effect of these measures is to further reduce employee loyalty to the organisation. To the extent that productivity is measurable, it sags. The false positives arising from data analytics explode because of the leap in negative sentiments expressed on internal networks and in social media, and in the vituperative language that the postings contain. The false positives greatly increase the size of the haystack, making the presumed needles even harder to find. The poisonous atmosphere increases the opportunities for a vindictive insider to obfuscate their activities and even to find willing collaborators. Eventually, cool heads prevail by pointing out how few individuals ever actually leak information without authority.

The wave of over-reaction slowly subsides, leaving a bruised and dissatisfied workforce with a bad taste in its mouth. [Section 4].

You must answer the following questions, clearly indicating which question you are answering within your submission. The page lengths suggested for each question here are for guidance only; the given page length limit for the overall assignment is mandatory.

Question 1. (1 page) Consider the ACS code of conduct. For each of the six values, taking account of any relevant sub-parts, discuss whether the value was demonstrated in the scenario and to what extent. If you assess any value as largely irrelevant to the scenario, then a very brief reason for this assessment is sufficient.

Question 2. (1/2 page) Consider the 10 UNESCO Principles [S III.2]. Looking closely at Principle Proportionality and Do No Harm [p20], discuss how this principle is applied (or not) in the scenario and identify any potential harm that might have ensued.

Question 3. (2 pages) Consider the numbered guidelines in Table 2 of Clarke’s Guidelines for the responsible application of data analytics. From every segment (1 General, 2 Data Acquisition, 3 Data analysis, and 4 Use of the Inferences), choose one guideline that you consider would have been applied in the scenario. Its application may not be explicit in the scenario description, but it should be relevant and important to the scenario, and you can argue that it was applied properly and therefore did not contribute to the negative consequences of the scenario. Explain its role in the scenario, including how it would have contributed to positive outcomes. Justify why it is more relevant than every one of the other guidelines that you consider would have been applied in the same segment. Argue how it is more or less relevant than any guidelines in the same segment that you consider may have been disregarded in the scenario. Be careful to consider the intention of the guidelines rather than an overly literal interpretation; you may rephrase the chosen guideline for the scenario context where beneficial. For further explanation of this point, see Section 3 in Clarke’s Guidelines.

Question 4. (1 page) (a) Choose one, numbered guideline (e.g. guideline 3.3) in Table 2 of the Guidelines that you consider to have been disregarded in the scenario. You may choose any guideline that you did not choose for Question 3. Discuss how the failure to consider the guideline could have contributed to the negative outcome of the scenario. (b) In addition, identify any other potential consequences that could have occurred due to the failure to consider that same guideline. For this purpose, the consequences you identify are not necessarily explicit within the scenario description. You might find it helpful to think of this activity as contributing to a risk assessment process before your hypothetical involvement in the analysis work of the scenario.

Question 5. (1 page) Consider the paper by Du et al, Techniques for Interpretable Machine Learning. Discuss whether and how intrinsic and post-hoc interpretability techniques could be applied to the scenario and what benefits could ensue.