LLM Ethics Lab: Roleplay Scenarios Around Apple + Google Model Deals

Hook: Teach the reality, not the hype — when Siri plugs into Gemini

Students and club members struggle with scattered theory and a lack of realistic practice: how do you evaluate the ethics and contract risks when your app depends on a third‑party foundation model? The 2026 Apple–Google Siri+Gemini arrangement provides a live, high-stakes case study. Use this classroom pack to run roleplay simulations that teach ethics, contracts, and developer responsibility for real-world model dependencies.

Why this matters in 2026

In late 2025 and early 2026 we saw major shifts that make this lab essential for developer education:

• Big vendor partnerships: Apple announced a production integration relying on Google’s Gemini for next‑gen Siri capabilities, illustrating cross‑vendor dependence.
• Regulatory pressure: The EU AI Act is in force and many jurisdictions updated guidance on AI liability and transparency; US standards (NIST and federal guidance) tightened reporting expectations.
• Litigation and content licensing: Publishers filed suits and sought new licensing terms related to model training and usage — a reminder that content provenance matters for downstream products.
• Model supply chain risk: Watermarking, provenance APIs, and model versioning became central to product risk management.

Learning goals (for a single session)

• Understand ethical principles applied to third‑party models: fairness, transparency, accountability, and safety.
• Practice negotiating contract terms around model usage: data, updates, liability, and audit rights.
• Design developer responsibilities: testing, monitoring, incident response, and communication to users.
• Develop soft skills: stakeholder empathy, legal reasoning, and rapid decision‑making under pressure.

Audience & prerequisite knowledge

This pack is built for tech club meetings, undergraduate CS/SE classes, and mentoring cohorts. Participants should have basic familiarity with:

• APIs, REST concepts, and model integration patterns
• Software licensing basics and SLAs
• Ethics frameworks (e.g., fairness, privacy, transparency)

Session formats

Use one of these depending on time and cohort size:

• 90–120 minute live roleplay: Best for a single meetup or class session.
• Half‑day workshop (3–4 hours): Deeper contract drafting plus incident simulation and technical mitigation planning.
• Multi‑week club project: Create a public teaching repo with artifacts: negotiated contract, monitoring dashboard prototype, and postmortem template.

Materials & prep

1. Printed/Google Doc role cards (see below).
2. Sample contract clause bank (provided in this pack).
3. Incident scenarios and timeline cards.
4. Whiteboard or Miro board for mapping dependencies and communication flows.
5. Optional: small code repo showing a minimal Siri-like client calling a third‑party model API (Node/Python).

Core roleplay scenario: Siri uses Gemini (90–120 minutes)

Frame: Apple is rolling out a new Siri feature that relies on Google’s Gemini via an API partnership. The class splits into stakeholder teams and negotiates contracts, plans safety testing, and runs a simulated incident when a Gemini response causes reputational harm.

Roles

• Apple Product Manager (PM): Prioritizes user experience, brand trust, and deadlines.
• Google Gemini Engineer / Commercial Rep: Represents the model provider, focuses on uptime, model update cadence, and IP constraints.
• Publisher / Content Owner Lawyer: Demands clarity about training data provenance and licensing terms.
• App Developer / Platform Engineer: Responsible for integration, monitoring, and fallbacks.
• User Advocate / Privacy Officer: Presses for consent, explainability, and opt‑outs.
• Regulator / External Auditor (optional): Evaluates compliance with AI rules (e.g., EU AI Act) and demands explainability and logging.

Phases & timing

1. 0–15 min: Briefing — each role reads a one‑page brief with objectives and constraints.
2. 15–45 min: Negotiation sprint — teams pair up (Apple vs Google; Publisher vs Apple) to draft key clauses and risk mitigations.
3. 45–75 min: Safety planning — the App Developer designs a monitoring and rollout plan; User Advocate vets transparency language.
4. 75–100 min: Incident injection — facilitator reads an incident card (e.g., Gemini hallucination that quotes a paywalled article as fact or leaks user PII). Teams do a 20‑minute incident response and produce a public statement.
5. 100–120 min: Debrief — review decisions, discuss tradeoffs, and grade using the rubric.

Sample incident cards (pick one)

• Hallucination & defamation: Siri, while summarizing news, asserts a false claim about a public figure citing a named publisher, sparking complaints and a DMCA‑style takedown request.
• Privacy leak: An API prompt inadvertently includes user location history and the response contains identifiable private information.
• Biased outcome: Gemini suggestions systematically underrepresent a community in hiring or housing-related queries.
• Model update break: A new Gemini version changes tokenization and causes broken formatting in Siri’s generated UI, crashing some user flows.

What to negotiate: contract clauses (teaching bank)

Give students editable drafts. Focus on clauses that affect ethics and developer responsibilities:

• Service Level & Availability: Uptime commitments, maintenance windows, and rollback procedures for harmful model updates.
• Data Use & Privacy: Clear limits on what prompt data is retained, aggregation habits, PII handling, and on‑device caching rules.
• Model Provenance & Licensing: Vendor must disclose training data sources at an appropriate granularity or provide a model card and provenance metadata.
• Explainability & Transparency: Audit logs, model version headers in API responses, and mandatory “why this result?” metadata for high‑risk decisions.
• Security & Vulnerability Disclosure: Timeline for fixes, escalation points, and patch deployment windows.
• Liability & Indemnification: Caps on damages, carveouts for willful misconduct, and mutual indemnities for IP infringement claims.
• Audit & Inspection Rights: Right to third‑party audits and red‑team reports for high‑risk features.
• Model Update & Versioning: Notification periods for breaking changes and testing sandbox access before rollout.
• Termination & Transition Assistance: Data return/erase, portability of fine‑tuning artifacts, and migration support if the relationship ends.

Example clause (concise teaching version)

Model Update & Test Access: Vendor will provide a pre‑release sandbox for new major model versions at least 30 days prior to production rollout. Client may reject a release that fails agreed safety tests; vendor will remediate within 15 business days or support a rollback to the prior model at no additional cost.

Developer responsibilities checklist (practical takeaways)

After negotiation, teams should produce a short checklist developers will follow:

1. Implement deterministic request headers to capture model version, prompt hash, and user consent flags.
2. Build user‑facing disclosures: when content is generated by Gemini and how to provide feedback or opt out.
3. Create automated tests: hallucination detection heuristics, safety classifiers, and adversarial prompt tests.
4. Design observability: dashboards for anomalous outputs, latency spikes, and usage patterns tied to releases.
5. Run periodic red‑team exercises, including community bug bounties for model outputs that lead to harm.

Assessment rubric (useable for grading or mentoring)

Score each team 1–5 across these dimensions:

• Risk identification: Did they identify ethical, legal, and technical risks?
• Mitigation quality: Are mitigations practical and testable?
• Contract clarity: Are clauses specific, measurable, and enforceable?
• Incident response: Was the public statement clear, timely, and accountable?
• Stakeholder empathy: Did they address user and content owner concerns?

Debrief guide — key questions to ask

• Which tradeoffs were hardest to accept: speed to market vs deep auditing, or uptime guarantees vs ability to roll back a harmful model?
• How did legal constraints (like licensing claims) shape technical design choices?
• What monitoring signals gave early warning of the incident? What was missing?
• Who should talk to customers and the press? What balance of technical detail vs plain language is right?
• How would these decisions change under different regulatory frameworks (EU AI Act vs US guidance)?

Advanced extensions for multi‑week projects

Stretch assignments for deeper learning:

• Build a minimal integration: client app + middleware that inserts model provenance and mitigations (sandbox repo).
• Conduct a red‑team: create adversarial prompts and make a public report with remediation recommendations.
• Draft a community‑facing transparency report: model card, provenance summary, and metrics on false positives/negatives.
• Prototype a monitoring dashboard that correlates model version, latency, and safety flags.

Sample classroom artifacts to ship

• Negotiated contract excerpt (PDF/Markdown)
• Incident response timeline and public statement
• Developer checklist and test suite outline
• Postmortem template with root cause analysis

Real‑world anchors: what happened in 2025–2026 (context for discussion)

Use these trends as discussion prompts. In 2024–2026, platform partnerships (like Apple tapping Google’s Gemini) highlighted cross‑vendor dependencies. Publishers pursued litigation and licensing discussions in late 2025, prompting questions about training data provenance and downstream rights. Regulators pushed for stronger transparency and auditability in 2025–26 — a real reminder that designers and developers must account for legal and reputational risk, not just technical performance.

“When your product’s intelligence comes from an external model, your responsibility to users doesn’t disappear.” — Teaching point for facilitators

Practical code & testing exercises

For mentors who want a hands‑on coding component, include these small tasks (30–90 minutes each):

• Implement request logging middleware that records model_version, prompt hash, and response confidence score.
• Create a small classifier to detect hallucinations by cross‑referencing claims against a trusted knowledge base or factcheck API.
• Build a synthetic load test showing how model latency spikes affect UX and how graceful degradation works (fallback to simpler rule‑based responses).

Teaching tips & mentor prompts

• Encourage role fidelity: students play their roles’ incentives, even if they personally disagree.
• Bring a neutral facilitator to inject incidents and keep the negotiation time‑boxed.
• Use real regulatory text snippets (EU AI Act, NIST guidance) to ground conversations in real obligations.
• After the roleplay, run a quick anonymous poll on which team’s approach participants trust most and why.

Assessment — evidence of learning

Look for these deliverables as proof that students internalized the topic:

• A short risk register listing top 5 risks and measurable mitigations.
• Contract excerpt with at least 3 enforceable protections (versioning, audit rights, data retention).
• A public incident statement that balances transparency with legal caution.
• Code artifact showing logging, version tagging, or a basic hallucination detector.

Community challenges & mentorship ideas

Turn this into an inter‑club or online community challenge:

• Host an inter‑university negotiation tournament: teams submit negotiated contract excerpts and incident responses; judges score on safety and enforceability.
• Open a mentor review board: upload artifacts and request feedback from industry volunteers (legal, product, security).
• Publish a shared template library: contract clauses, monitoring checklists, and postmortem formats under a Creative Commons license for reuse by other cohorts.

Further reading & resources (2026‑relevant)

• EU AI Act — obligations on high‑risk systems and transparency (reference for compliance discussions).
• NIST AI Risk Management Framework updates — practical guidance for risk assessment and monitoring.
• Model cards and datasheets literature — how to present provenance and limitations to partners and users.
• Recent reporting on the Apple–Google partnership and publisher litigation (late 2025/early 2026) — use as current events for debate.

Wrap up: what students will walk away with

By running this lab, participants gain a practical toolkit for designing and shipping products that rely on third‑party models. They will be able to:

• Negotiate enforceable contract provisions that reduce legal and ethical exposure.
• Design developer responsibilities that make monitoring and incident response realistic.
• Critically evaluate vendor claims (model cards, provenance) and translate them into testable requirements.

Call to action

Use this pack at your next class or club meeting. Clone the starter repo, run the 90‑minute roleplay, and publish your artifacts to invite peer review. Share outcomes with our mentorship community to get industry feedback — and help build a living library of real‑world teaching materials that keep pace with trends like the Siri‑Gemini partnership and evolving AI regulation.

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