COMP90087 · The Ethics of Artificial Intelligence
Generative AI and Ethics
Week 12 is the capstone: it turns the whole semester’s toolkit on everyday use of chat LLMs — AI companionship and relationships, academic integrity and cheating, and the health of our shared knowledge systems — and the 1500-word research essay is due this week. The lecture organises the implications into two categories, knowledge/epistemic and wellbeing/flourishing, and adds “technological affordances” as an analytic lens. Expect it to synthesise bias, transparency, governance and the three theories onto frontier cases.
What this chapter covers
- 01Two categories of implications of everyday chat-LLM use: knowledge/epistemic and wellbeing/flourishing
- 02Knowledge implications: information habits, belief formation, information access, and the health of the shared web
- 03Wellbeing implications: relationships with machines, dependence, authenticity, meeting psycho-social needs
- 04Technological affordances — what a technology invites, enables or constrains us to do — as a transferable analytic lens
- 05AI companionship and relationships (“Who Do We Become When We Talk to Machines?”)
- 06Academic integrity and cheating; the subject’s own GenAI policy
- 07Recurring threads synthesised from earlier weeks: generative bias (W9), authorship/labour (W6), LLM transparency (W8), data governance of prompts (W10)
- 08The 1500-word research essay (30%) applying the whole toolkit to an AI use case, due this week
Analyse an everyday LLM-use case with the two-category lens and affordances
- +1Knowledge/epistemic implications. Outsourcing summarising and drafting can reshape information habits and belief formation — the student may stop verifying sources or lose the skill of building an argument, and heavy reliance can degrade the shared web that the model itself depends on. This draws on transparency/epistemic-dependence themes (W8).
- +1Wellbeing/flourishing implications. Using the LLM as a late-night confidant touches psycho-social needs, authenticity and dependence — companionship with a machine may or may not support genuine flourishing (recall Tasioulas’ participation: well-being needs active engagement, not passive receipt).
- +1Apply the affordances lens. Ask what the tool invites, enables and constrains: it invites quick outsourcing, enables 24/7 availability, and constrains effortful reasoning and human contact — a transferable analysis that doesn’t require judging the tool as simply good or bad.
- +1Bring in academic integrity and the GenAI policy. Summarising to understand is fine, but pasting AI-generated content into submitted work breaches the subject’s GenAI policy and is academic misconduct — and the closed-book hurdle exam is precisely designed so the understanding must be the student’s own. Land a reflective, reasoned position.
Key terms
- Knowledge / epistemic implications
- How everyday chat-LLM use affects information habits, learning, belief formation and the shared systems for producing and sharing knowledge — including the health of the web the models draw on.
- Wellbeing / flourishing implications
- How chat-LLM use affects our capacity to meet psycho-social needs and find meaning — relationships with machines, dependence and authenticity — echoing Tasioulas’ point that flourishing needs active participation.
- Technological affordances
- What a technology invites, enables or constrains us to do. A transferable analytic lens for many technologies, letting you analyse a tool without simply labelling it good or bad.
- AI companionship
- The use of chatbots for relationship-like interaction and emotional support, raising questions of authenticity, dependence and what we become when we routinely talk to machines rather than people.
- GenAI policy (subject)
- The subject’s rules: GenAI may be used for initial structuring, planning and grammar, but including AI-generated content in submitted work, passing AI text as your own, or giving intermediate work to AI for review is prohibited, and any permitted use must be declared.
- Academic misconduct
- Serious integrity breaches — presenting others’ or AI’s work as your own, self-plagiarism, fabricating information, or failing to acknowledge sources — which the closed-book hurdle exam and GenAI policy are designed to deter.
Generative AI and Ethics FAQ
What’s the two-category framework for analysing chat-LLM use?
The lecture splits the implications into knowledge/epistemic (how tools affect information habits, learning, belief formation and the shared web) and wellbeing/flourishing (how they affect psycho-social needs, relationships, dependence and authenticity). Pairing these with the affordances lens — what a tool invites, enables and constrains — gives you a reusable structure for any generative-AI case, including the research essay.
How does Week 12 tie the whole subject together?
It applies earlier frameworks to frontier LLM cases: generative bias (W9), authorship and labour (W6), transparency of LLMs (W8), data governance of prompts and training data (W10), and the three theories throughout. The examinable skill is synthesis — turning the semester’s toolkit on your own AI habits and reaching a reflective, reasoned position rather than a hot take.
What are the rules on using GenAI in the research essay?
Use it only within the subject’s stated policy and declare any permitted use. Generally you may use GenAI for initial structuring, planning, brainstorming and grammar, but you must not include AI-generated content in the submitted work, submit AI text as your own, or give intermediate work to an AI for review. Misuse is academic misconduct — confirm the current policy on Canvas.
Can AI help me with the Week-12 research essay for COMP90087?
Yes, as a tutor within the policy. Sia can help you structure an argument, apply the two-category and affordances lenses, and pressure-test your thesis on a GenAI case — explaining each step and checking your reasoning. It is built to mirror how the University of Melbourne assesses this and does not write the essay you submit; putting AI-generated content into your essay breaches the subject’s GenAI policy.
Exam move
Approach Week 12 as synthesis: for any GenAI case, split the analysis into knowledge/epistemic and wellbeing/flourishing implications, structure it with the affordances lens (invites / enables / constrains), and tag each point to the earlier week it draws on (bias, transparency, governance, a theory). This is also your research-essay scaffold — commit to one theory and reach a reasoned verdict. Keep the subject’s GenAI policy clear in your mind, both because it is examinable content and because the essay is due this week and the exam is a closed-book hurdle designed to test your own understanding. Confirm essay logistics and the policy on Canvas.
Working through Generative AI and Ethics in COMP90087? Sia is AskSia’s AI AI Ethics tutor — ask any COMP90087 Generative AI and Ethics question and get a clear, step-by-step explanation grounded in how COMP90087 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
