Interactive Activity: Stakeholder Impact Lab for AI Ethics
Purpose
Design a structured, role-based simulation where participants analyze an AI deployment, map stakeholder impacts, propose risk mitigations, and produce an “Ethical Deployment Brief” with measurable safeguards. The activity emphasizes practical application of recognized AI ethics and risk management guidance (e.g., NIST AI Risk Management Framework 1.0, OECD AI Principles, UNESCO Recommendation on the Ethics of AI, ISO/IEC 23894:2023). It is suitable for graduate learners and professionals.
Learning outcomes (measurable)
- Identify and prioritize stakeholders and impacted rights for a specific AI use case.
- Map potential harms and benefits across the AI lifecycle (data, model, deployment, monitoring).
- Propose specific, feasible mitigations and oversight mechanisms aligned to stated principles.
- Define measurable safeguards and monitoring triggers (e.g., fairness, robustness, privacy, and transparency controls).
- Document trade-offs and decision rationales in an auditable format.
Format and timing
- Group size: 4–6 per team; 3–8 teams.
- Duration: 90 minutes total (60-minute and 120-minute variants noted below).
- Modality: In-person or virtual (supports breakout rooms and shared templates).
Materials
- Case brief (provided by facilitator; see example below).
- Role cards: Product Owner, ML Lead, HR/Operations Lead, Legal/Compliance, Data Protection Officer, Candidate/Consumer Advocate.
- Templates (one set per team):
- Stakeholder Map (primary/secondary stakeholders, power/interest grid).
- Lifecycle Impact Register (data → training → validation → deployment → monitoring; risk description, severity, likelihood, affected rights).
- Mitigation Matrix (preventive, detective, corrective controls; owner; due date).
- Metrics and Monitoring Plan (fairness, privacy, robustness, transparency indicators; thresholds; frequency; escalation).
- Ethical Deployment Brief (summary, commitments, residual risks, sign-offs).
- Timer, shared digital workspace, polling tool.
Pre-session preparation (optional, 20–30 minutes)
- Short primer summarizing high-level concepts from:
- NIST AI RMF 1.0 (risk framing, measurement, mitigation, monitoring).
- OECD AI Principles (inclusive growth, human-centered values, transparency, robustness, accountability).
- ISO/IEC 23894:2023 (AI risk management process).
- Brief guidance on basic fairness notions (e.g., checking outcome disparities across protected groups), data minimization, and human-in-the-loop oversight.
Scenario (sample)
Automated Resume Screening System: An HR technology vendor offers a model that scores applicant resumes for a large retailer to prioritize candidates for phone screens. Data sources include historical hiring outcomes, resumes, and optional social profiles. The retailer wants to reduce time-to-hire by 30% and improve “fit.” Jurisdictions include regions with anti-discrimination and data protection regulations. The vendor plans to integrate with applicant tracking systems.
Procedure and timing
- Framing and norms (5 minutes)
- State objectives, outputs, and decision documentation requirement.
- Emphasize respectful dialogue and use of hypothetical data. No personal data sharing.
- Values calibration poll (5 minutes)
- Quick poll: “Speed vs. fairness,” “Accuracy vs. privacy,” “Automation vs. human oversight.” Teams discuss how these tensions may surface in the scenario.
- Role assignment and case read (5 minutes)
- Distribute role cards and case brief. Assign a facilitator and a scribe per team.
- Stakeholder mapping (10 minutes)
- Identify primary and secondary stakeholders (e.g., applicants, hiring managers, HR, compliance, community groups).
- Use a power/interest grid to prioritize engagement and oversight.
- Lifecycle impact analysis (20 minutes)
- For each lifecycle stage, enumerate potential harms/benefits and affected rights:
- Data collection: consent, sensitive attributes, data representativeness, provenance.
- Training/validation: label bias, performance variance across groups, leakage.
- Deployment: user interface prompts, candidate notice, appeal/contestability, human review thresholds.
- Monitoring: drift detection, incident response, complaint handling.
- Rate severity and likelihood. Note uncertainties and data gaps.
- Mitigation design and metrics (20 minutes)
- Select controls and define how they will be measured:
- Fairness controls: sampling or reweighting, calibrated thresholds, post-processing adjustments; report parity metrics and confidence intervals for key subgroups.
- Privacy controls: data minimization, purpose limitation, retention schedule, de-identification or differential privacy parameters where appropriate.
- Transparency: candidate notice, plain-language model card, data usage summary, explanation method suitable for the context.
- Accountability: human-in-the-loop review criteria, escalation paths, audit logging, change management, third-party assessment schedule.
- Safety/robustness: stress tests, adversarial evaluation, fallback procedures for system failure.
- Define thresholds and triggers (e.g., if disparity between groups exceeds predefined limits, initiate review and mitigation within a set timeframe).
- Ethical Deployment Brief and peer review (15 minutes)
- Compile the brief: context, stakeholder map, top risks, selected mitigations, metrics and monitoring plan, residual risk rationale, and sign-offs.
- Swap briefs with another team. Apply a checklist:
- Are stakeholders and rights adequately covered?
- Are risks lifecycle-complete and prioritized?
- Are mitigations specific, feasible, and assigned?
- Are metrics measurable and monitored on a realistic cadence?
- Are residual risks and trade-offs explicitly justified?
- Whole-group debrief (10 minutes)
- Discuss differences in trade-offs and how evidence, principles, and constraints shaped decisions.
- Identify open questions and data or governance gaps that require escalation.
Deliverables
- Stakeholder Map (with engagement priorities).
- Lifecycle Impact Register (top 5–8 risks with ratings and uncertainties).
- Mitigation Matrix (controls with owners and timelines).
- Metrics and Monitoring Plan (indicators, thresholds, frequency, escalation).
- Ethical Deployment Brief (2–3 pages) suitable for governance review.
Assessment rubric (10-point scale, adaptable)
- Stakeholder coverage and rights analysis (0–2).
- Completeness and prioritization of lifecycle risks (0–2).
- Specificity and feasibility of mitigations with ownership (0–2).
- Measurement plan quality (indicators, thresholds, monitoring cadence) (0–2).
- Clarity of residual risk rationale and traceability to principles/frameworks (0–2).
Facilitation guidance
- Keep teams on time using visible timers.
- Prompt for evidence and traceability: “Which principle or risk category does this address?” “How will you know it works?”
- Intervene on unsupported claims; request data, pilots, or uncertainty notation.
- Ensure inclusion: rotate speaking opportunities, invite advocate roles early.
- Avoid legal advice; encourage documenting compliance questions for specialist review.
Accessibility and inclusion
- Provide all materials in accessible digital formats (screen-reader compatible, high-contrast, large-print versions).
- Offer remote participation via captioned video, shared documents, and structured turn-taking.
- Use plain language in briefs; minimize jargon and define terms.
Risk and ethics safeguards for the activity
- Use fictional or sanitized cases and synthetic data.
- Prohibit sharing real candidate or employee data.
- Establish discussion norms to prevent stereotyping or bias reinforcement.
Variations
- Technical track: Add hands-on fairness auditing with sample metrics on a toy dataset.
- Policy track: Replace Step 6 with drafting a governance policy addendum and an incident response playbook.
- 60-minute version: Combine Steps 4 and 5 (15 minutes), shorten Step 6 (10 minutes), and reduce peer review (5 minutes).
- 120-minute version: Add evidence-gathering sprints (e.g., identify needed data, design a small A/B pilot and evaluation plan).
Suggested references for alignment (non-exhaustive)
- NIST AI Risk Management Framework 1.0 (risk identification, measurement, mitigation, and governance).
- OECD AI Principles (human-centered values, transparency, robustness, accountability).
- UNESCO Recommendation on the Ethics of AI (human rights–based approach and societal well-being).
- ISO/IEC 23894:2023 (process guidance for AI risk management).
Note
This activity supports learning about ethical analysis and governance of AI systems. It does not substitute for legal or regulatory advice.
