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Ethical AI Assessment for Social Protection - Module 7.5 Track D

⚖️ Ethical AI Assessment for Social Protection

Comprehensive evaluation framework for predictive social protection systems - systematically assess fairness, transparency, privacy, accountability, and impact to identify and address ethical risks before deployment.

Project Information

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Questions Answered
0/15
Overall Score
0
Critical Risks
0
Completion
0%
⚖️ Dimension 1: Fairness & Non-Discrimination
0/100
1.1 Training Data Diversity
33 points

Is your training data representative of all demographic groups in your target population?

Excellent: Fully representative with oversampling of minority groups +33
Good: Generally representative with known limitations +20
Fair: Some groups underrepresented but documented HIGH RISK +10
Poor: Significant demographic gaps not assessed CRITICAL +0
1.2 Bias Testing & Auditing
33 points

How frequently do you test for demographic bias in model predictions?

Excellent: Systematic audits quarterly with external validation +33
Good: Regular internal testing (bi-annually) +20
Fair: One-time assessment during development HIGH RISK +10
Poor: No systematic bias testing conducted CRITICAL +0
1.3 Fairness Across Demographics
34 points

Does your system achieve similar accuracy across gender, ethnicity, geography, and disability status?

Excellent: ≤5% accuracy gap across all groups +34
Good: 5-10% accuracy gap with mitigation strategies +20
Fair: 10-15% gap, gaps documented MEDIUM RISK +10
Poor: >15% gap or unknown disparities CRITICAL +0
🔍 Dimension 2: Transparency & Explainability
0/100
2.1 Decision Explainability
40 points

Can the system explain to beneficiaries why they received a specific decision?

Excellent: Individual explanations with feature contributions in plain language +40
Good: General criteria provided, limited personalization +25
Fair: Generic explanation available for staff only HIGH RISK +10
Poor: Black box - no explanations provided CRITICAL +0
2.2 Algorithm Transparency
30 points

Is information about the algorithm and decision criteria publicly available?

Excellent: Full documentation public with model cards +30
Good: High-level methodology published +18
Fair: Available upon request only MEDIUM RISK +8
Poor: Proprietary/confidential - not disclosed HIGH RISK +0
2.3 Performance Reporting
30 points

Do you publicly report model performance metrics and limitations?

Excellent: Quarterly public reports with subgroup metrics +30
Good: Annual reporting with overall metrics +18
Fair: Internal reporting only MEDIUM RISK +8
Poor: No performance reporting HIGH RISK +0
🔒 Dimension 3: Privacy & Data Protection
0/100
3.1 Data Minimization
25 points

Do you collect only the minimum data necessary for the specific purpose?

Excellent: Strict minimization with regular necessity reviews +25
Good: Purpose-limited with documented justification +15
Fair: Some unnecessary data collected "just in case" MEDIUM RISK +7
Poor: Collect all available data without assessment HIGH RISK +0
3.2 Security & Encryption
25 points

What level of security protects beneficiary data?

Excellent: End-to-end encryption, multi-factor auth, regular pen testing +25
Good: Strong encryption at rest and in transit +15
Fair: Basic encryption, some vulnerabilities HIGH RISK +7
Poor: Minimal/no encryption, weak access controls CRITICAL +0
3.3 Informed Consent
25 points

How do you obtain consent for AI-based decision making?

Excellent: Clear, specific consent in multiple formats/languages +25
Good: Written consent with explanation of AI use +15
Fair: Generic consent buried in terms MEDIUM RISK +7
Poor: Assumed/implied consent without disclosure CRITICAL +0
3.4 Data Retention & Deletion
25 points

Do you have clear policies for data retention and secure deletion?

Excellent: Automated deletion after defined period, audit trails +25
Good: Clear retention policy with manual deletion +15
Fair: Vague policy, ad-hoc deletion HIGH RISK +7
Poor: Indefinite retention, no deletion process CRITICAL +0
👁️ Dimension 4: Accountability & Oversight
0/100
4.1 Human-in-the-Loop
35 points

What level of human oversight exists for AI decisions?

Excellent: Mandatory human review for all benefit denials +35
Good: Human review for low-confidence predictions +20
Fair: Sampling-based human review only HIGH RISK +10
Poor: Fully automated with no human oversight CRITICAL +0
4.2 Appeals Process
35 points

Can beneficiaries appeal AI decisions? How accessible is the process?

Excellent: Clear, free, accessible appeals with independent review +35
Good: Formal appeals process, some barriers (time, complexity) +20
Fair: Limited appeals, high barriers HIGH RISK +10
Poor: No appeals mechanism exists CRITICAL +0
4.3 Error Detection & Correction
30 points

How do you identify and correct AI errors?

Excellent: Active monitoring with rapid correction protocols +30
Good: Periodic audits with documented correction process +18
Fair: Reactive correction based on complaints MEDIUM RISK +8
Poor: No systematic error tracking CRITICAL +0
📊 Dimension 5: Impact Assessment
0/100
5.1 Risk Assessment
50 points

Have you conducted a comprehensive risk/impact assessment before deployment?

Excellent: Independent impact assessment with mitigation plans +50
Good: Internal risk assessment with documented findings +30
Fair: Basic assessment, limited scope HIGH RISK +12
Poor: No formal risk assessment conducted CRITICAL +0
5.2 Community Involvement
50 points

Were affected communities involved in system design and governance?

Excellent: Co-design with beneficiaries, ongoing advisory role +50
Good: Consultation during design phase +30
Fair: Limited stakeholder engagement HIGH RISK +12
Poor: Top-down design without community input CRITICAL +0

📊 Assessment Results

Priority Recommendations

4-Week Ethical Implementation Plan

Week 1: Ethical Assessment & Planning
• Complete comprehensive ethical audit of current system
• Assemble multi-stakeholder ethics committee
• Create detailed remediation plan for critical risks
• Establish governance framework and accountability structures
Week 2: Critical Fixes & Privacy Enhancement
• Address all CRITICAL risks (score of 0) - no deployment until resolved
• Implement privacy controls (encryption, consent, access control)
• Establish fairness monitoring dashboards
• Create explainability mechanisms for decisions
Week 3: Testing, Validation & Community Engagement
• Test with diverse user groups across demographics
• Conduct bias audits on all protected characteristics
• Validate consent and appeals processes
• Gather community feedback and iterate
Week 4: Documentation, Training & Launch Preparation
• Finalize ethical guidelines and operating procedures
• Train all staff on ethical AI principles and oversight
• Establish ongoing monitoring and audit procedures
• Prepare public transparency reporting