Ethics Safety Impact

Ethics, Safety & Impact Assessment

Table of Contents

• Workflow
• Common Patterns
• Guardrails
• 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 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 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 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 for prioritization framework.

Step 5: Design mitigations and safeguards

For high-priority harms, propose design changes, policy safeguards, oversight mechanisms. See resources/methodology.md 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. 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 who can halt harmful features.

8. Respect autonomy and consent: Provide informed choice in plain language, meaningful alternatives (not coerced consent), user control (opt out, delete data), and purpose limitation. Children and vulnerable groups need extra protections.

Common pitfalls:

• ❌ Assuming "we treat everyone the same" = fairness: Equal treatment of unequal groups perpetuates inequality. Fairness often requires differential treatment.
• ❌ Optimization without constraints: Maximizing engagement/revenue unconstrained leads to amplifying outrage, addiction, polarization. Set ethical boundaries.
• ❌ Moving fast and apologizing later: For safety/ethics, prevention > apology. Harms to vulnerable groups are not acceptable experiments.
• ❌ Privacy theater: Requiring consent without explaining risks, or making consent mandatory for service, is not meaningful consent.
• ❌ Sampling bias in testing: Testing only on employees (young, educated, English-speaking) misses how diverse users experience harm.
• ❌ Ethics washing: Performative statements without material changes. Impact assessments must change decisions, not just document them.

Quick Reference

Key resources:

• resources/template.md: Stakeholder mapping, harm/benefit analysis, risk matrix, mitigation planning, monitoring framework
• resources/methodology.md: Fairness metrics, privacy analysis, safety assessment, bias detection, participatory design
• resources/evaluators/rubric_ethics_safety_impact.json: Quality criteria for stakeholder analysis, harm identification, mitigation design, monitoring

Stakeholder Priorities:

High-risk groups to always consider:

• Children (<18, especially <13)
• People with disabilities (visual, auditory, motor, cognitive)
• Racial/ethnic minorities, especially historically discriminated groups
• Low-income, unhoused, financially precarious
• LGBTQ+, especially in hostile jurisdictions
• Elderly (>65), especially digitally less-skilled
• Non-English speakers, low-literacy
• Political dissidents, activists, journalists in repressive contexts
• Refugees, immigrants, undocumented
• Mentally ill, cognitively impaired

Harm Categories:

• Physical: Injury, death, health deterioration
• Psychological: Trauma, stress, anxiety, depression, addiction
• Economic: Lost income, debt, poverty, exclusion from opportunity
• Social: Discrimination, harassment, ostracism, loss of relationships
• Autonomy: Coercion, manipulation, loss of control, dignity violation
• Privacy: Surveillance, exposure, data breach, re-identification
• Reputational: Stigma, defamation, loss of standing
• Epistemic: Misinformation, loss of knowledge access, filter bubbles
• Political: Disenfranchisement, censorship, targeted repression

Fairness Definitions (choose appropriate for context):

• Demographic parity: Outcome rates equal across groups (e.g., 40% approval rate for all)
• Equalized odds: False positive and false negative rates equal across groups
• Equal opportunity: True positive rate equal across groups (equal access to benefit)
• Calibration: Predicted probabilities match observed frequencies for all groups
• Individual fairness: Similar individuals treated similarly (Lipschitz condition)
• Counterfactual fairness: Outcome same if sensitive attribute (race, gender) were different

Mitigation Strategies:

• Prevent: Design change eliminates harm (e.g., don't collect sensitive data)
• Reduce: Decrease likelihood or severity (e.g., rate limiting, friction for risky actions)
• Detect: Monitor and alert when harm occurs (e.g., bias dashboard, anomaly detection)
• Respond: Process to address harm when found (e.g., appeals, human review, compensation)
• Safeguard: Redundancy, fail-safes, circuit breakers for critical failures
• Transparency: Explain, educate, build understanding and trust
• Empower: Give users control, choice, ability to opt out or customize

Monitoring Metrics:

• Outcome disparities: Measure by protected class (approval rates, error rates, treatment quality)
• Error distribution: False positives/negatives, who bears burden?
• User complaints: Volume, categories, resolution rates, disparities
• Engagement/retention: Differences across groups (are some excluded?)
• Safety incidents: Volume, severity, affected populations
• Consent/opt-outs: How many decline? Demographics of decliners?

Escalation Triggers:

• Disparate impact >20% without justification
• Safety incidents causing serious harm (injury, death)
• Vulnerable group disproportionately affected (>2× harm rate)
• User complaints spike (>2× baseline)
• Press/regulator attention
• Internal ethics concerns raised

When to escalate beyond this skill:

• Legal compliance required (GDPR, ADA, Civil Rights Act, industry regulations)
• Life-or-death safety-critical system (medical, transportation)
• Children or vulnerable populations primary users
• High controversy or political salience
• Novel ethical terrain (new technology, no precedent) → Consult: Legal counsel, ethics board, domain experts, affected communities, regulators

Inputs required:

• Feature or decision (what is being proposed? what changes?)
• Affected groups (who is impacted? direct and indirect?)
• Context (what problem does this solve? why now?)

Outputs produced:

• ethics-safety-impact.md: Stakeholder analysis, harm/benefit assessment, fairness evaluation, risk prioritization, mitigation plan, monitoring framework, escalation protocol