代做COMP4920 Applied Ethics: AI Case Study Term 3 2025帮做Python语言程序
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Applied Ethics: AI Case Study
Term 3 2025
1 Outline
Your team of size 2-4 (assigned in weeks 1-2 tutorials) has been tasked with conducting a conceptual Value Sensitive Design (VSD) project for the product owner of a new AI technology of your choosing. Your technology should be either recently deployed, or soon to be, and you are looking to see how adopting a Fairness, Accountability, Transparency, Ethics (FATE) framework is important to resolving conflict between stakeholders. The resources you need for this task are covered in the lecture content, the readings, and your tutorials in weeks 3-5.
2 Brief
• You must deliver an oral presentation outlining your method and results. see: https://www.student.unsw.edu.au/support-oral-presentations
• You must deliver a report presenting your method and results. see: https://www.student.unsw.edu.au/report-writing-support
• You are to conduct your own research into how this task is to proceed. see: HERE for a good start (pp. 74—79), also HERE
• You are to apportion these tasks between yourselves.
• You will be given a portion of each tutorial to progress this project.
• You should consult with your tutor continually throughout this process.
End of Brief
Note: Essay 2 will involve tasks concerning your group report (among other things), so doing the group assessment properly is essential for your passing the hurdle task.
3 Specifications
3.1 Format
3.1.1 Oral Presentation
Due Date: Slides submitted by Week 7 - Monday (27/10/2025 09:00)
(note: presentations will be held in your tutorials over weeks 7/8)
Word Count: NA, your allocated time-slot is 20 minutes
Template: None, use your own initiative to conform to the brief
Contributions: You are marked as a group.
3.1.2 Written Report
Due Date: Week 8 - Friday (7/11/2025 17:00)
Word Count: 1500 words per group member (±10%)
Template: None, use your own initiative to conform to the brief
Contributions: You are marked individually based on your contribution.
(note: please make it clear who contributes where)
3.2 Competency Grading
(CN) - Non-Competent
Context, technology, stakeholders unclear, missing, poorly outlined. Analysis superficial, descriptive rather than critical and reflective - lacking in literature support or meaningful consideration of similar technologies or relevant values. Recommendations unfounded, unconnected to analysis. Deliverables are presented unprofessionally with limited attempts at formatting as outlined in the brief.
(CO) - Competent
Context, technology, stakeholders adequately outlined. Analysis identifies key values, some conflicts addressed through comparison with similar technologies - literature engagement consistent throughout. Creative, critical thinking present, recommendations sound but possibly undeveloped. Deliverables are presented professionally, in conformance with the brief.
(CM) - Competent with Merit
Succinct, well structured introduction covering context, technology, stakeholders in a broad and engaging manner. Analysis critical and sophisticated, comparison with similar technologies supported and extended through ample engagement with relevant literature. Recommendations are predicated upon a sustained, reflective argument derived directly from the conceptual analysis. Deliverables exceed expectations, all aspects of the presentation and report are of an outstanding, professional standard, publishable in a conference or journal.
3.3 Course Learning Outcomes
This assignment will contribute to the following Course Learning outcomes:
• CLO1 : Articulate the major normative and meta-ethical theories that underpin real, research level moral debates in both academic and professional contexts.
• CLO2 : Define and employ ethical values, principles, and practices for responsible research and innovation of technological and computing advances.
• CLO3 : Build, articulate, and justify their own moral arguments - as well as how to analyse moral judgements and moral arguments in general.
• CLO4 : Discuss ethical dilemmas around specific technological case studies.
