COMP90087 · The Ethics of Artificial Intelligence
The Complete Exam Bible · S1 2027The Ethics of Artificial Intelligence
COMP90087 The Ethics of Artificial Intelligence is the University of Melbourne's Master's-level subject in the philosophy of AI, and it is built in two movements. Weeks 1-5 assemble a moral-reasoning toolkit — the history of AI, the ethics-versus-law distinction and principlism (beneficence, non-maleficence, justice, respect for autonomy), then the three classical theories: utilitarianism (consequences), deontology (duty) and virtue ethics (character). Weeks 6-12 apply that toolkit to live AI problems — creative economies, trust and accountability, transparency and explainability, algorithmic bias and equity, data governance, contemporary issues (governance, sustainability, right to repair) and generative AI. It is a reasoning-and-writing subject: marks come from applying a theory to an AI case, arguing a clear position, and recalling who-owns-which-concept, not from calculation. Assessment runs through Canvas as four components: tutorial participation and online discussion (10%, 1% per tutorial across Weeks 2-12, best 10 of 12 count); a Week-6 closed-book handwritten in-class essay (30%, ~90 minutes, ~1000 words, applying Weeks 1-5 theory to a scenario); a 1500-word take-home research essay (30%, due Week 12); and a 120-minute closed-book digital exam (30%) sat on campus via the LockDown browser, with roughly 30-40 multiple-choice questions (including “select all that apply”) plus 3-4 short-answer questions. The exam is a hurdle requirement: score 49% or below on it and you fail the whole subject (grade NH), no matter how strong your essays are. This 12.5-credit-point subject feeds the Weighted Average Mark (WAM), and its H1 / H2A / H2B / H3 / P grade bands sit behind every result.
What COMP90087 covers
COMP90087 builds a moral-reasoning toolkit — principlism plus utilitarianism, deontology and virtue ethics — in Weeks 1-5, then applies it to accountability, transparency, algorithmic bias, data governance and generative AI in Weeks 7-12. It is assessed by tutorial participation (10%), a Week-6 closed-book in-class essay (30%), a Week-12 research essay (30%) and a closed-book examination-period exam (30%) that is a hurdle you must pass. This map follows the real UniMelb teaching order so you can revise each week toward those assessments.
How COMP90087 is assessed
| Component | Weight | Format |
|---|---|---|
| Tutorial participation & online discussion | 10% | 1% per tutorial across weeks 2-12 (best 10 of 12 count); in-person attendance plus posting group answers to the Discussion Board within 24 hrs · see GenAI policy |
| In-class handwritten essay | 30% | ~90-minute closed-book handwritten argumentative essay (~1000 words) in Week 6 lecture time; applies Weeks 1-5 theory to an AI scenario |
| Research essay | 30% | 1500-word individual take-home argumentative essay on an AI use case; due Week 12 · see GenAI policy |
| Exam | 30% | 120-minute closed-book digital exam (LockDown browser, on-campus) during the examination period; ~30-40 MCQ + 3-4 short-answer questions |
One AI case, three theories: how utilitarianism, deontology and virtue ethics diverge
- +1Frame the case and name the variant of each theory before reasoning. Utilitarianism (consequences / principle of utility, here act-utilitarian), deontology (Kant's Formula of Humanity plus Ross's duties), virtue ethics (Aristotelian character + care ethics). State that a real essay commits to ONE and goes deep — planning all three is a revision drill.
- +1Utilitarian run: tally harms and benefits weighted by magnitude, probability and scope. Benefits = less cheating, lower staff load (broad but modest). Harms = wrongful accusation, anxiety and disproportionate false-positive burden on neurodivergent students (severe, concentrated). Net verdict tends toward “modify or drop” because the concentrated harm outweighs a diffuse convenience gain — but a utilitarian could keep it if the cheating harm were large enough, which is exactly the theory's weakness.
- +1Deontological run: apply the Formula of Humanity — flagging students on unexplainable grounds and denying them a way to contest treats them merely as means (data-subjects) rather than as ends who are owed reasons and consent. Ross's duty of non-maleficence and fidelity (honest process) also bite. Verdict: keeping an opaque, uncontestable tool is impermissible regardless of the efficiency payoff.
- +1Virtue-ethics run: ask what a person of practical wisdom (phronesis) would do, and which character traits the choice expresses. Deploying a tool you cannot explain, that predictably burdens a vulnerable group, shows a deficiency of justice and care and an excess of trust in automation. The virtuous institution audits, keeps a human in the loop, and errs toward care. Verdict: augment human judgement, do not automate the accusation.
- +1Show the divergence honestly: deontology and virtue ethics converge on “don't keep it as-is,” while a utilitarian verdict is contingent on the numbers — so the theories can disagree on the same facts, and even two utilitarians can differ. This is the examinable meta-point: no theory is a decision procedure you can run blindly; conclusions must be argued out with facts, logic and reasons.
- +1Turn it into an essay move: commit to one theory, use the case's concrete features (the opacity, the disparate false-positive rate) as evidence, evaluate rather than list, raise and rebut the strongest opposing view (e.g. the utilitarian efficiency defence), and reach a reasoned conclusion. Land a position such as “keep a transparent, contestable, human-audited version or withdraw it.”
Key terms
- Principlism
- Applying four classic bioethics principles — beneficence (do good), non-maleficence (avoid harm), justice (fairness) and respect for autonomy — to AI decisions. Named explicitly in the in-class essay prompt; the four principles are the scaffold for evaluating a deployment.
- Principle of utility
- The utilitarian criterion (Bentham/Mill): the right action maximises overall happiness — the greatest total balance of pleasure over suffering across everyone affected, counted impartially. In an AI case you sum benefits and harms and pick the option with the greatest net utility.
- Categorical imperative (Formula of Humanity)
- Kant's duty-based test: always treat rational beings as ends in themselves, never merely as means. Grounds AI limits on deception, non-consensual data use and uncontestable automated decisions, independent of good outcomes.
- Phronesis (practical wisdom)
- In Aristotle's virtue ethics, the master virtue needed to apply the other virtues to concrete situations and adjudicate when they conflict. Shannon Vallor recasts it as “technomoral wisdom” for technology design.
- Accountability (Nissenbaum)
- Being answerable for an outcome: blameworthy or praiseworthy, liable to sanction or restitution, and able to give an account of what was done. Nissenbaum names four barriers that erode it in computerised systems (many hands, bugs treated as inevitable, computer-as-scapegoat, ownership without liability).
- Hurdle requirement
- An assessment component you must pass on its own to pass the subject, regardless of your total. In COMP90087 the 120-minute exam is a hurdle: 49% or below on it means grade NH (fail), even with strong essays.
COMP90087 FAQ
Is COMP90087 hard?
It is conceptually rich rather than mathematically hard — there is essentially no calculation beyond one trivial utility sum. The real demand is breadth and precision: you must hold a lot of named theories, thinkers, definitions and case studies straight (who said what — phronesis is Aristotle, technomoral virtues are Vallor, Gilligan and Tronto are care ethics), and you must argue rather than summarise. Two essays make up 60% of the mark and reward a clear defended position, while the closed-book exam is a hurdle you must pass. Students who revise week by week, rehearse applying each theory to a fresh AI scenario, and practise the ~40-word short-answer structure tend to find it manageable; leaving it to SWOTVAC is risky because the recall load is large. Steady work also protects your WAM.
Can AI help me with COMP90087?
Yes, as a step-by-step study aid. Sia is an AI tutor built to mirror how COMP90087 is actually taught and assessed at the University of Melbourne: it can walk you through applying utilitarianism, deontology or virtue ethics to an AI case, help you map concepts to the right thinker, structure an argumentative essay plan, and rehearse the point-by-point short-answer format — explaining each step and checking your reasoning as you go. Bring your own scenario or past question and ask it to talk you through the method. It does not do graded assessment for you, and University of Melbourne academic-integrity rules apply — use it to understand how to argue, not to generate work you submit (the subject's GenAI policy forbids putting AI-generated content in your essays).
Where can I find past exam papers / practice for COMP90087?
Start on Canvas, where the subject posts its exam-information page and any released sample questions, and search the University of Melbourne Library's past-examination-paper collection for available papers. Your weekly tutorial materials and the lecturers' released essay feedback are the closest match to the exam's short-answer and essay style. This guide also includes a re-authored practice exam that mirrors the paper's shape — MCQ (including “select all that apply”), concept-to-thinker matching and ~40-word short answers — with fresh stems and numbers, and you can ask Sia to generate extra practice in the same style and explain each step. Confirm what is officially provided on Canvas.
What are the COMP90087 hurdles and assessment rules?
There is one hurdle: the 120-minute closed-book exam (30%). You must score at least 50% on it to pass the subject; 49% or below gives grade NH (a fail) regardless of your essays, so the exam is not optional cramming — it gates the whole subject. The other three components (tutorial participation 10%, in-class essay 30%, research essay 30%) are weighted but not hurdles. Tutorial marks are 1% per tutorial across Weeks 2-12 with the best 10 of 12 counting, so you can miss one without penalty. Confirm the exact rules, the examination-period date and permitted materials on your Canvas assessment page and your personal University of Melbourne exam timetable.
Can I use GenAI in my COMP90087 essays?
Only within the subject's stated policy, and you must declare any permitted use. The course generally allows GenAI for initial structuring, planning, brainstorming and grammar, but forbids including any AI-generated content in submitted work, submitting AI text as your own, or giving intermediate work to an AI for review. The rationale is that it defeats the learning, produces generic answers that lack philosophical depth, and the closed-book exam is a hurdle you must pass on your own. Treat a tool like Sia as a way to understand the theories and rehearse arguments — not to produce the essay — and check the current GenAI policy on Canvas, because misuse is academic misconduct.
How to study for the exam
Treat COMP90087 as two linked jobs — build the toolkit (Weeks 1-5), then apply it (Weeks 6-12) — and rehearse both weekly rather than cramming through SWOTVAC, because the recall load (theories, thinkers, definitions, cases) is the real difficulty. First, lock the three-theory map cold: utilitarianism = consequences, deontology = duty, virtue ethics = character, with the signature machinery under each (principle of utility; Kant's Formula of Humanity and Ross's prima facie duties; Aristotle's golden mean and phronesis, plus Vallor's technomoral virtues and care ethics). Make a who-said-what sheet, because the exam repeatedly tests attribution (phronesis = Aristotle, technomoral virtues = Vallor, Gilligan and Tronto = care ethics, which item is NOT a GDPR principle). Second, drill application: take one AI scenario a week and argue it once under each theory so you feel where they agree and diverge — that is the exact skill both essays are marked on. Third, practise the two written formats: the argumentative essay (clear position, define your theory, apply it to case detail, rebut one objection, reasoned conclusion — evaluate, don't list) and the ~40-word short answer (one mark per component: state the concept, say why it matters ethically, give an example). Because the exam is a closed-book hurdle you must pass at 50%, prioritise being able to start every topic over perfecting a few. When a distinction won't stick, ask Sia to explain it a different way and set you a fresh practice item; it teaches the method and checks your reasoning, and it never substitutes for your own graded work. Confirm the examination-period date, room and rules on Canvas and the University of Melbourne exam timetable.
Your AI AI Ethics tutor for COMP90087
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