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Ethical AI Assessment Framework - Module 7.4 Track C

⚖️ Ethical AI Assessment Framework for Social Services

Systematically evaluate the ethical implications of your AI social service project across privacy, bias, consent, impact, accountability, and cultural dimensions.

Project Information

Start by providing basic information about your AI project

Assessment Progress

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Questions Answered
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Overall Score
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Critical Risks
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Completion
🔒 Data Privacy & Protection
0/100
Why This Matters: Health and education data are highly sensitive. Vulnerable populations (children, refugees, patients) have limited ability to protect themselves from data misuse. Strong privacy protections aren't optional—they're fundamental to ethical AI.
1.1
Data Collection Minimization
Do you collect only the minimum data necessary for your AI system to function?
Strict Minimization
Collect only absolutely essential data, regularly review and delete unnecessary fields
+25
Reasonable Minimization
Collect necessary data plus some useful additional fields
+15
Broad Collection
Collect comprehensive data "just in case" it's useful later
+5
No Minimization Policy
Haven't considered data minimization
+0
1.2
Data Storage & Encryption
How is sensitive data stored and protected?
End-to-End Encryption + On-Device
Data encrypted at rest and in transit, processed on-device when possible
+25
Strong Encryption
Data encrypted at rest and in transit with strong algorithms
+15
Basic Security
Password protection and HTTPS, but no encryption at rest
+5
Minimal Security
Data stored without encryption
+0 CRITICAL RISK
1.3
Data Retention & Deletion
Do you have clear policies for how long data is kept and when it's deleted?
Clear Retention Policy + Automated Deletion
Documented retention periods, automated deletion, user can request deletion anytime
+25
Retention Policy Exists
Have documented retention periods but deletion is manual
+15
No Formal Policy
Delete data on request but no systematic approach
+5
Indefinite Retention
Keep data indefinitely
+0 HIGH RISK
1.4
Third-Party Data Sharing
Do you share user data with third parties (cloud providers, partners, etc.)?
No Third-Party Sharing
All data processing on-device or on controlled infrastructure
+25
Limited Sharing with Contracts
Share with vetted partners under strict data processing agreements
+15
Multiple Third Parties
Share with several partners (analytics, cloud providers, etc.)
+5
Unrestricted Sharing
Share data without strict controls
+0 CRITICAL RISK
✋ Consent & Autonomy
0/100
Why This Matters: Meaningful consent requires understanding. In low-literacy contexts or with vulnerable populations, obtaining true informed consent is challenging but essential. Users must have real choice and control.
2.1
Informed Consent Process
How do you obtain consent from users?
Multi-Modal Informed Consent
Explain in local language, using visual aids, with opportunity to ask questions, documented consent
+33
Clear Written Consent
Provide clear consent form in appropriate language
+20
Basic Terms & Conditions
Standard T&C that users must accept
+10
Assumed Consent
By using the system, users implicitly consent
+0 CRITICAL RISK
2.2
Right to Withdraw
Can users easily withdraw consent and have their data deleted?
Easy Withdrawal Anytime
Users can withdraw and delete data anytime through simple process
+33
Withdrawal Possible
Users can withdraw but process requires contacting support
+20
Difficult Withdrawal
Withdrawal possible but complicated or unclear process
+10
No Withdrawal Option
Once consented, cannot withdraw
+0 CRITICAL RISK
2.3
Transparency About AI Use
Do users know they're interacting with AI and understand how it works?
Full Transparency
Clear disclosure that AI is used, how it works, limitations, accuracy rates
+34
Basic Disclosure
Mention AI is used but limited explanation of how
+20
Vague Disclosure
Mention "technology" or "algorithms" without specifics
+10
No Disclosure
Don't inform users that AI is being used
+0 HIGH RISK
⚖️ Bias & Fairness
0/100
Why This Matters: AI systems can perpetuate and amplify existing societal biases, particularly affecting marginalized groups. In health and education, biased AI can worsen inequalities by providing worse service to those who need it most.
3.1
Training Data Diversity
How diverse is your training data across demographics?
Representative & Balanced
Training data represents all user demographics proportionally
+33
Mostly Diverse
Good diversity but some groups underrepresented
+20
Limited Diversity
Training data from limited demographic groups
+10 HIGH RISK
Not Assessed
Haven't evaluated training data diversity
+0 CRITICAL RISK
3.2
Bias Testing & Auditing
Do you regularly test your AI for biased outcomes?
Systematic Bias Audits
Regular testing across demographics, documented results, remediation process
+33
Periodic Testing
Test for bias during development and occasionally in production
+20
One-Time Testing
Tested for bias once during development
+10 MEDIUM RISK
No Bias Testing
Haven't tested for biased outcomes
+0 CRITICAL RISK
3.3
Fairness Across Languages
Does your AI perform equally well across all languages it supports?
Equal Performance Across Languages
Verified equivalent accuracy and quality in all supported languages
+34
Tested Major Languages
Main languages tested, some variation in quality
+20
English/Dominant Language Only
Optimized for one language, others are translations
+10 HIGH RISK
Single Language Only
Only supports one language
+0
👁️ Accountability & Oversight
0/100
Why This Matters: AI should augment, not replace, human judgment in critical decisions. Clear accountability prevents "automation bias" where people blindly trust AI, especially important when AI makes mistakes that affect health or education outcomes.
4.1
Human-in-the-Loop for Critical Decisions
Are important decisions reviewed by qualified humans?
Mandatory Human Review
All critical decisions (diagnosis, student placement, etc.) require professional review
+50
AI Suggests, Human Decides
AI provides recommendations, humans make final call
+30
Human Can Override
AI makes decisions but humans can override
+10 MEDIUM RISK
Fully Automated
AI makes decisions without human review
+0 CRITICAL RISK
4.2
Error Detection & Correction
How do you detect and correct AI errors?
Active Monitoring & Feedback Loop
Monitor AI performance, user feedback mechanism, regular model updates based on errors
+50
Periodic Review
Review AI errors periodically and update as needed
+30
Reactive Only
Fix errors when reported but no systematic monitoring
+10 HIGH RISK
No Error Tracking
Don't systematically track or correct AI errors
+0 CRITICAL RISK
📊 Impact Assessment
0/100
Why This Matters: Unintended consequences can harm the very people you're trying to help. Systematic impact assessment helps identify and mitigate risks before they cause damage.
5.1
Risk Assessment Conducted
Have you systematically assessed potential harms?
Comprehensive Impact Assessment
Documented assessment of potential harms with mitigation strategies
+50
Basic Risk Analysis
Identified main risks but not comprehensive
+30
Informal Discussion
Discussed risks but not documented
+10 HIGH RISK
No Risk Assessment
Haven't assessed potential harms
+0 CRITICAL RISK
5.2
Community Involvement in Design
Did affected communities participate in designing the AI system?
Co-Design with Community
Community members actively involved in design decisions throughout process
+50
Community Consultation
Consulted community members and incorporated feedback
+30
Limited Input
Informal conversations with some community members
+10 MEDIUM RISK
No Community Involvement
Designed without community input
+0 HIGH RISK
🎯
Overall Ethical Score
0
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🔒
Privacy & Protection
0
-
✋
Consent & Autonomy
0
-
⚖️
Bias & Fairness
0
-
👁️
Accountability
0
-
📊
Impact Assessment
0
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💡 Priority Recommendations

🗓️ 4-Week Ethical Implementation Plan