Template

AI Ethics Case Study & Decision Log Template

Interactive template to record AI ethics decisions, trade-offs, mitigation steps, owners, monitoring plans, and review timelines — designed for experiments, pilots, and production policy choices.

Interactive Tool

AI Ethics Case Study & Decision Log

Purpose

This interactive template helps teams make AI ethics decisions explicit, document trade-offs, capture mitigation steps, assign ownership, and schedule reviews so decisions are transparent and learnable.

When to use

Use for experiments, pilot deployments, procurement or policy choices involving models, data, AI services, or automation that affect people, operations, safety, privacy, compliance, or reputation.

How to use

  1. Complete each field with concise, evidence-based information.
  2. Be explicit about risks and measurable monitoring metrics.
  3. Assign an owner responsible for follow-up and monitoring.
  4. Set a review date and frequency to reassess outcomes and update the log.

Tip: Keep entries factual and link to supporting artifacts (data samples, model cards, test results) in Related Documents.

A short unique identifier (e.g., AI-2026-001).
Concise name for the project, experiment, or policy decision.
Use YYYY-MM-DD.
Choose the stage that best describes current status.
Describe purpose, users, stakeholders, affected populations, system boundaries, and expected impact.
What outcome does this system intend to enable or improve?
List teams, roles, and external stakeholders who are affected or responsible.
Describe data types, origin, sensitivity (PII, health, financial), and any consent or licensing constraints.
Name model families, vendor services, versions, or toolchains involved. Include links to model cards or vendor docs in Related Documents.
Select all that apply.
Provide detail when you selected 'Other'.
Scale where 1 = Low and 5 = Critical. Consider likelihood and potential impact.
1.0 10.0
Yes if particular groups may be disadvantaged or misrepresented.
Describe steps, technical controls, process changes, tests, datasets, review gates, or contractual terms to reduce risks. Be specific and actionable.
Choose the decision and, where applicable, add conditions or acceptance criteria below.
Explain the basis for the decision, references to tests or evidence, and any trade-offs considered.
List measurable criteria that must be met before broader rollout.
Name role(s) or persons responsible for implementation and monitoring. Include contact information or team.
Outline next steps, timelines, and dependencies for implementing the decision.
Define what will be measured, frequency, thresholds for alerts, and who monitors. Include plans for periodic audits and user feedback channels.
List specific metrics (e.g., error rates, fairness metrics by group, false positives/negatives, support ticket volume) and targets.
Date to re-evaluate the decision (YYYY-MM-DD).
Choose how often this decision should be reassessed.
Paste URLs or references to model cards, test results, data inventories, vendor contracts, or meeting notes.
Any other observations, unresolved questions, or follow-ups.
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