ethics-safety-impact

What this skill does

# Ethics, Safety & Impact Assessment

## Table of Contents
- [Workflow](#workflow)
- [Common Patterns](#common-patterns)
- [Guardrails](#guardrails)
- [Quick Reference](#quick-reference)

## Example

**Scenario**: Launching credit scoring algorithm for loan approvals

1. **Stakeholders**: Loan applicants (diverse demographics), lenders, society (economic mobility)
2. **Harms**: Disparate impact from historical bias, opacity preventing appeals, feedback loops perpetuating denials
3. **Vulnerable groups**: Racial minorities, immigrants with thin credit files, young adults, people in poverty
4. **Mitigations**: Fairness audit across protected classes, reason codes + appeals, alternative data (rent/utilities), human review for edge cases
5. **Monitoring**: Approval rate parity within 10% across groups; if disparate impact >20%, escalate to ethics committee

## Workflow

Copy this checklist and track your progress:

```
Ethics & Safety Assessment Progress:
- [ ] Step 1: Map stakeholders and identify vulnerable groups
- [ ] Step 2: Analyze potential harms and benefits
- [ ] Step 3: Assess fairness and differential impacts
- [ ] Step 4: Evaluate severity and likelihood
- [ ] Step 5: Design mitigations and safeguards
- [ ] Step 6: Define monitoring and escalation protocols
```

**Step 1: Map stakeholders and identify vulnerable groups**

Identify all affected parties (direct users, indirect, society). Prioritize vulnerable populations most at risk. See [resources/template.md](resources/template.md#stakeholder-mapping-template) for stakeholder analysis framework.

**Step 2: Analyze potential harms and benefits**

Brainstorm what could go wrong (harms) and what value is created (benefits) for each stakeholder group. See [resources/template.md](resources/template.md#harm-benefit-analysis-template) for structured analysis.

**Step 3: Assess fairness and differential impacts**

Evaluate whether outcomes, treatment, or access differ across groups. Check for disparate impact. See [resources/methodology.md](resources/methodology.md#fairness-metrics) for fairness criteria and measurement.

**Step 4: Evaluate severity and likelihood**

Score each harm on severity (1-5) and likelihood (1-5), prioritize high-risk combinations. See [resources/template.md](resources/template.md#risk-matrix-template) for prioritization framework.

**Step 5: Design mitigations and safeguards**

For high-priority harms, propose design changes, policy safeguards, oversight mechanisms. See [resources/methodology.md](resources/methodology.md#mitigation-strategies) for intervention types.

**Step 6: Define monitoring and escalation protocols**

Set metrics, thresholds, review cadence, escalation triggers. Validate using [resources/evaluators/rubric_ethics_safety_impact.json](resources/evaluators/rubric_ethics_safety_impact.json). **Minimum standard**: Average score ≥ 3.5.

## Common Patterns

**Pattern 1: Algorithm Fairness Audit**
- **Stakeholders**: Users receiving algorithmic decisions (hiring, lending, content ranking), protected groups
- **Harms**: Disparate impact (bias against protected classes), feedback loops amplifying inequality, opacity preventing accountability
- **Assessment**: Test for demographic parity, equalized odds, calibration across groups; analyze training data for historical bias
- **Mitigations**: Debiasing techniques, fairness constraints, explainability, human review for edge cases, regular audits
- **Monitoring**: Disparate impact ratio, false positive/negative rates by group, user appeals and overturn rates

**Pattern 2: Data Privacy & Consent**
- **Stakeholders**: Data subjects (users whose data is collected), vulnerable groups (children, marginalized communities)
- **Harms**: Privacy violations, surveillance, data breaches, lack of informed consent, secondary use without permission, re-identification risk
- **Assessment**: Map data flows (collection → storage → use → sharing), identify sensitive attributes (PII, health, location), consent adequacy
- **Mitigations**: Data minimization (collect only necessary), anonymization/differential privacy, granular consent, user data controls (export, delete), encryption
- **Monitoring**: Breach incidents, data access logs, consent withdrawal rates, user data requests (GDPR, CCPA)

**Pattern 3: Content Moderation & Free Expression**
- **Stakeholders**: Content creators, viewers, vulnerable groups (targets of harassment), society (information integrity)
- **Harms**: Over-moderation (silencing legitimate speech, especially marginalized voices), under-moderation (allowing harm, harassment, misinformation), inconsistent enforcement
- **Assessment**: Analyze moderation error rates (false positives/negatives), differential enforcement across groups, cultural context sensitivity
- **Mitigations**: Clear policies with examples, appeals process, human review, diverse moderators, cultural context training, transparency reports
- **Monitoring**: Moderation volume and error rates by category, appeal overturn rates, disparate enforcement across languages/regions

**Pattern 4: Accessibility & Inclusive Design**
- **Stakeholders**: Users with disabilities (visual, auditory, motor, cognitive), elderly, low-literacy, low-bandwidth users
- **Harms**: Exclusion (cannot use product), degraded experience, safety risks (cannot access critical features), digital divide
- **Assessment**: WCAG compliance audit, assistive technology testing, user research with diverse abilities, cross-cultural usability
- **Mitigations**: Accessible design (WCAG AA/AAA), alt text, keyboard navigation, screen reader support, low-bandwidth mode, multi-language, plain language
- **Monitoring**: Accessibility test coverage, user feedback from disability communities, task completion rates across abilities

**Pattern 5: Safety-Critical Systems**
- **Stakeholders**: End users (patients, drivers, operators), vulnerable groups (children, elderly, compromised health), public safety
- **Harms**: Physical harm (injury, death), psychological harm (trauma), property damage, cascade failures affecting many
- **Assessment**: Failure mode analysis (FMEA), fault tree analysis, worst-case scenarios, edge cases that break assumptions
- **Mitigations**: Redundancy, fail-safes, human oversight, rigorous testing (stress, chaos, adversarial), incident response plans, staged rollouts
- **Monitoring**: Error rates, near-miss incidents, safety metrics (accidents, adverse events), user-reported issues, compliance audits

## Guardrails

1. **Identify vulnerable groups explicitly**: Prioritize children, elderly, people with disabilities, marginalized/discriminated groups, low-income, low-literacy, geographically isolated, and politically targeted populations. If none are identified, look harder.

2. **Consider second-order and long-term effects**: Look for feedback loops (harm leads to disadvantage leads to more harm), normalization, precedent-setting, and accumulation of small harms over time. Ask "what happens next?"

3. **Assess differential impact, not just average**: A feature may help the average user but harm specific groups. Check for disparate impact (outcome differences across groups >20% is a red flag), intersectionality, and distributive justice.

4. **Design mitigations before launch**: Build safeguards into design, test with diverse users, use staged rollouts with monitoring, and pre-commit to audits. Reactive fixes come too late for those already harmed.

5. **Provide transparency and recourse**: At minimum, explain decisions, provide appeal mechanisms with human review, offer redress for harm, and maintain audit trails.

6. **Monitor outcomes, not just intentions**: Measure outcome disparities by group, user-reported harms, error rate distribution, and unintended consequences. Set thresholds that trigger review or shutdown.

7. **Establish clear accountability and escalation**: Define who reviews ethics risks before launch, who monitors post-launch, what triggers escalation, and w