AI/ML Systems:
├── Fairness - Equitable treatment across groups
├── Explainability - Understandable decisions
├── Reliability - Consistent, predictable behavior
├── Safety - Prevent harm, fail safely
├── Privacy - Protect personal data
├── Security - Resist adversarial attacks
├── Inclusiveness - Accessible to all users
└── Human Control - Meaningful human oversight

┌─────────────────────────────────────────────────────────────┐
│                  Ethical Impact Assessment                   │
├─────────────────────────────────────────────────────────────┤
│  1. Describe     │  System purpose, capabilities, context   │
├──────────────────┼──────────────────────────────────────────┤
│  2. Stakeholder  │  Identify all affected parties           │
│     Analysis     │  Map interests and concerns              │
├──────────────────┼──────────────────────────────────────────┤
│  3. Impact       │  Assess benefits and harms               │
│     Assessment   │  Evaluate likelihood and severity        │
├──────────────────┼──────────────────────────────────────────┤
│  4. Ethical      │  Apply ethical principles                │
│     Analysis     │  Identify conflicts and tensions         │
├──────────────────┼──────────────────────────────────────────┤
│  5. Mitigation   │  Design controls and safeguards          │
│     Planning     │  Define monitoring approach              │
├──────────────────┼──────────────────────────────────────────┤
│  6. Decision &   │  Approve, modify, or reject              │
│     Review       │  Schedule ongoing review                 │
└─────────────────────────────────────────────────────────────┘

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Direct Harms:
├── Physical harm to individuals
├── Psychological harm (stress, manipulation)
├── Financial harm (fraud, loss)
├── Privacy harm (exposure, surveillance)
├── Discrimination harm (unfair treatment)
└── Autonomy harm (manipulation, coercion)

Indirect/Systemic Harms:
├── Environmental harm
├── Democratic harm (manipulation, division)
├── Economic harm (displacement, inequality)
├── Social harm (erosion of trust, relationships)
└── Cultural harm (homogenization, loss)

Group-Specific Harms:
├── Harm to marginalized groups
├── Harm to vulnerable populations
├── Harm to future generations
└── Harm to non-users

               REVERSIBILITY
               Easy    Difficult   Permanent
S      Low     1          2           3
E      Medium  2          4           6
V      High    3          6           9
E      Extreme 4          8          12
R
I
T
Y

Score:
1-2:  Acceptable with monitoring
3-4:  Requires mitigation
6-8:  Significant controls required
9-12: May be unacceptable

public class AiEthicsChecklist
{
    public List<EthicsCheckItem> GetChecklist()
    {
        return new List<EthicsCheckItem>
        {
            // Fairness
            new("FAIR-01", "Bias Testing",
                "Has the model been tested for bias across protected groups?",
                EthicsCategory.Fairness, Priority.Critical),
            new("FAIR-02", "Fairness Metrics",
                "Are fairness metrics defined and monitored?",
                EthicsCategory.Fairness, Priority.High),
            new("FAIR-03", "Training Data",
                "Is training data representative and free from historical bias?",
                EthicsCategory.Fairness, Priority.Critical),

            // Transparency
            new("TRANS-01", "Explainability",
                "Can the system explain its decisions to affected users?",
                EthicsCategory.Transparency, Priority.High),
            new("TRANS-02", "AI Disclosure",
                "Are users informed they are interacting with AI?",
                EthicsCategory.Transparency, Priority.Critical),
            new("TRANS-03", "Limitation Disclosure",
                "Are system limitations clearly communicated?",
                EthicsCategory.Transparency, Priority.High),

            // Human Control
            new("CTRL-01", "Human Oversight",
                "Is there meaningful human oversight of AI decisions?",
                EthicsCategory.HumanControl, Priority.Critical),
            new("CTRL-02", "Override Capability",
                "Can humans override AI decisions when needed?",
                EthicsCategory.HumanControl, Priority.High),
            new("CTRL-03", "Escalation Path",
                "Is there a clear escalation path for concerning outputs?",
                EthicsCategory.HumanControl, Priority.High),

            // Safety
            new("SAFE-01", "Harm Prevention",
                "Are there safeguards against harmful outputs?",
                EthicsCategory.Safety, Priority.Critical),
            new("SAFE-02", "Fail-Safe Design",
                "Does the system fail safely when errors occur?",
                EthicsCategory.Safety, Priority.High),
            new("SAFE-03", "Adversarial Testing",
                "Has the system been tested against adversarial inputs?",
                EthicsCategory.Safety, Priority.High),

            // Privacy
            new("PRIV-01", "Data Minimization",
                "Does the system collect only necessary data?",
                EthicsCategory.Privacy, Priority.High),
            new("PRIV-02", "Consent",
                "Is there informed consent for data use?",
                EthicsCategory.Privacy, Priority.Critical),
            new("PRIV-03", "Data Protection",
                "Is personal data adequately protected?",
                EthicsCategory.Privacy, Priority.Critical),

            // Accountability
            new("ACCT-01", "Responsibility",
                "Is there clear ownership for system outcomes?",
                EthicsCategory.Accountability, Priority.High),
            new("ACCT-02", "Audit Trail",
                "Are decisions logged for accountability?",
                EthicsCategory.Accountability, Priority.High),
            new("ACCT-03", "Redress Mechanism",
                "Is there a way for affected parties to seek redress?",
                EthicsCategory.Accountability, Priority.High)
        };
    }
}

Ethics Review Board Composition:
├── Chair (Senior Leadership)
├── Ethics Officer (if applicable)
├── Technical Lead (understands the technology)
├── Legal Representative
├── Privacy Officer
├── Business Representative
├── External Ethicist (optional but recommended)
└── User/Community Representative (for significant decisions)

public enum EthicsDecision
{
    Approved,                    // Proceed as designed
    ApprovedWithConditions,      // Proceed after specified changes
    RequiresRedesign,           // Fundamental changes needed
    Deferred,                   // Need more information
    Rejected,                   // Unacceptable ethical risk
    EscalateToExecutive         // Beyond board authority
}

public class EthicsReviewResult
{
    public required EthicsDecision Decision { get; init; }
    public required string Rationale { get; init; }
    public List<string> Conditions { get; init; } = new();
    public List<string> MonitoringRequirements { get; init; } = new();
    public DateTimeOffset? NextReviewDate { get; init; }
    public List<BoardMemberVote> Votes { get; init; } = new();
}

Stage 1: Ideation
├── Initial ethics screening
├── Identify potential concerns
└── Go/No-Go for research

Stage 2: Research & Design
├── Stakeholder analysis
├── Preliminary impact assessment
└── Ethics-by-design integration

Stage 3: Development
├── Ongoing ethics review
├── Testing for bias/harm
└── Documentation

Stage 4: Pre-Deployment
├── Full ethical impact assessment
├── Board review (if triggered)
└── Mitigation verification

Stage 5: Deployment
├── Monitoring plan activation
├── Feedback mechanisms
└── Incident response ready

Stage 6: Operations
├── Ongoing monitoring
├── Regular reviews
└── Continuous improvement