Building an Ethics Checklist for Your ML Projects
An ML ethics checklist turns review into a repeatable process: every model gets checked for data quality, fairness, interpretability, and deployment safety before it ships.
Over the last four lessons, you've learned to audit datasets for bias, measure fairness with metrics, interpret model predictions, and assess deployment risk. Now it's time to package all of that into something you'll actually use: a reusable ethics checklist.
Sixteen checks across five phases. That's the whole artifact this capstone produces, and every line of it traces back to one of the previous four lessons. Your team probably already runs a PR review checklist: tests pass, no accessibility regressions, performance budget met, documentation updated. Nobody relies on memory alone for quality assurance, and your ML projects shouldn't either.
Learning Objectives
- ○Combine dataset auditing, fairness metrics, interpretability, and deployment review into a single checklist
- ○Build a typed, executable ethics checklist in TypeScript
- ○Understand when each check should be applied in the ML lifecycle
- ○Create a reusable template for future ML projects
The PR Checklist for ML
Frontend
Pull Request Review Checklist
// PR checklist: tests pass, a11y checked, perf budget met, docs updatedMachine Learning
ML Ethics Checklist
// Ethics checklist: data audited, fairness tested, model explained, impact assessedYour PR checklist catches bugs before they reach production. An ML ethics checklist catches harm before it reaches users. Both work because they make quality systematic rather than optional.
The Five Phases of Ethics Review
Phase 1: Data Sourcing
Before you touch a model, audit your data.
interface CheckItem {
id: string;
phase: 'data' | 'training' | 'evaluation' | 'deployment' | 'monitoring';
description: string;
status: 'pass' | 'fail' | 'not-applicable' | 'pending';
notes: string;
}
interface EthicsChecklist {
projectName: string;
modelVersion: string;
reviewDate: string;
reviewer: string;
checks: CheckItem[];
overallStatus: 'approved' | 'needs-review' | 'blocked';
}
const ethicsTemplate: CheckItem[] = [
// Phase 1: Data
{
id: 'data-source',
phase: 'data',
description: 'Data sources are documented with collection methods and dates',
status: 'pending',
notes: '',
},
{
id: 'data-consent',
phase: 'data',
description: 'Data was collected with appropriate consent and licensing',
status: 'pending',
notes: '',
},
{
id: 'data-representation',
phase: 'data',
description: 'Dataset representation audit completed: no group below 10% of expected proportion',
status: 'pending',
notes: '',
},
{
id: 'data-labels',
phase: 'data',
description: 'Labels reviewed for measurement bias and historical bias',
status: 'pending',
notes: '',
},
// Phase 2: Training
{
id: 'training-splits',
phase: 'training',
description: 'Train/validation/test splits maintain demographic proportions',
status: 'pending',
notes: '',
},
{
id: 'training-augmentation',
phase: 'training',
description: 'Data augmentation does not introduce or amplify bias',
status: 'pending',
notes: '',
},
// Phase 3: Evaluation
{
id: 'eval-fairness',
phase: 'evaluation',
description: 'Fairness metrics computed per demographic group (disparate impact ratio >= 0.8)',
status: 'pending',
notes: '',
},
{
id: 'eval-subgroup',
phase: 'evaluation',
description: 'Performance metrics broken down by subgroup: no group accuracy below threshold',
status: 'pending',
notes: '',
},
{
id: 'eval-interpretability',
phase: 'evaluation',
description: 'Feature importance reviewed: model uses relevant features, not proxies',
status: 'pending',
notes: '',
},
// Phase 4: Deployment
{
id: 'deploy-model-card',
phase: 'deployment',
description: 'Model card completed with intended use, limitations, and performance metrics',
status: 'pending',
notes: '',
},
{
id: 'deploy-impact',
phase: 'deployment',
description: 'Impact assessment completed: all critical failure modes have mitigations',
status: 'pending',
notes: '',
},
{
id: 'deploy-fallback',
phase: 'deployment',
description: 'Human fallback process defined for high-stakes decisions',
status: 'pending',
notes: '',
},
{
id: 'deploy-recourse',
phase: 'deployment',
description: 'Users affected by model decisions have a clear appeal or recourse process',
status: 'pending',
notes: '',
},
// Phase 5: Monitoring
{
id: 'monitor-drift',
phase: 'monitoring',
description: 'Monitoring in place for data drift and model performance degradation',
status: 'pending',
notes: '',
},
{
id: 'monitor-fairness',
phase: 'monitoring',
description: 'Ongoing fairness metric tracking scheduled (monthly minimum)',
status: 'pending',
notes: '',
},
{
id: 'monitor-feedback',
phase: 'monitoring',
description: 'Feedback mechanism in place for users to report issues',
status: 'pending',
notes: '',
},
];Running the Review
function runEthicsReview(checklist: EthicsChecklist): string {
const phases = ['data', 'training', 'evaluation', 'deployment', 'monitoring'] as const;
const report: string[] = [];
report.push(`Ethics Review: ${checklist.projectName} v${checklist.modelVersion}`);
report.push(`Reviewer: ${checklist.reviewer} | Date: ${checklist.reviewDate}`);
report.push('---');
let hasFailures = false;
let hasPending = false;
for (const phase of phases) {
const phaseChecks = checklist.checks.filter(c => c.phase === phase);
const passed = phaseChecks.filter(c => c.status === 'pass').length;
const failed = phaseChecks.filter(c => c.status === 'fail').length;
const pending = phaseChecks.filter(c => c.status === 'pending').length;
report.push(`${phase.toUpperCase()}: ${passed} passed, ${failed} failed, ${pending} pending`);
for (const check of phaseChecks) {
const icon = check.status === 'pass' ? '[PASS]' :
check.status === 'fail' ? '[FAIL]' :
check.status === 'not-applicable' ? '[N/A]' : '[PENDING]';
report.push(` ${icon} ${check.description}`);
if (check.notes) report.push(` Note: ${check.notes}`);
}
if (failed > 0) hasFailures = true;
if (pending > 0) hasPending = true;
}
report.push('---');
if (hasFailures) {
report.push('OVERALL: BLOCKED; address failing checks before deployment');
} else if (hasPending) {
report.push('OVERALL: NEEDS REVIEW; complete pending checks');
} else {
report.push('OVERALL: APPROVED; all checks passed');
}
return report.join('\n');
}Making It Stick
The best checklist is one you actually use. Here are three tips from the frontend world:
-
Automate what you can. CI already runs your tests and linting without being asked; fairness metric computation and representation audits belong in the training pipeline the same way.
-
Treat the review as a merge gate. Your PR can't merge without passing tests, and your model shouldn't deploy without passing ethics review.
-
The checklist itself needs maintenance. Your PR checklist evolves as you learn from production incidents, and your ethics checklist should evolve the same way.
Challenge
Build a complete ethics checklist for an ML project and run the review.
Recall Prompt
Why should fairness audits and representation checks be in the CI pipeline rather than a manual pre-launch step?
Lesson Recap
What you learned
- ✓An ML ethics checklist spans five phases (data, training, evaluation, deployment, monitoring) and should function as a deployment gate, not an optional suggestion.
- ✓Automating what you can, specifically representation audits and fairness metric computation, removes the friction that causes manual reviews to be skipped.
- ✓The checklist itself needs versioning and iteration; as you learn from real-world outcomes, the checks should evolve to reflect new risks.
The bridge
Just as a pull request checklist enforces code quality gates (tests pass, accessibility checked, performance budget met) before merging, an ML ethics checklist enforces responsible AI gates before a model ships to users.
You can now
Build a typed, executable ethics checklist in TypeScript covering all five phases of the ML lifecycle and run it as a deployment review.