1.2 Core Ethical Principles (The North Star)

These five foundational principles serve as the ethical foundation for all AI activities within your organization. They should be embedded in every policy, process, and decision involving AI systems—from initial ideation through deployment and retirement.

🌐 Global Alignment

These principles align with major international frameworks including the OECD AI Principles (2019/2024), UNESCO Recommendation on AI Ethics, EU AI Act fundamental requirements, NIST AI RMF trustworthiness characteristics, and the G7 Code of Conduct for AI developers.

1.2.1 Fairness & Inclusiveness

Principle: AI systems must treat all individuals and groups equitably, avoiding unfair discrimination and promoting inclusive outcomes across diverse populations.

What This Means in Practice

Non-Discrimination: AI systems must not produce outcomes that unfairly disadvantage individuals or groups based on protected characteristics (race, gender, age, disability, religion, national origin, etc.)
Representation: Training data must adequately represent the populations the system will serve, including historically marginalized groups
Accessibility: AI systems must be designed to be usable by people with disabilities and across diverse technical capabilities
Equitable Access: Benefits of AI should be distributed fairly, not concentrated among privileged groups

Types of Bias to Address

Bias Type	Description	Mitigation Strategy
Historical Bias	Data reflects past discriminatory practices	Audit historical data for patterns; consider reweighting or supplementing
Representation Bias	Training data underrepresents certain groups	Stratified sampling; synthetic data augmentation; active data collection
Measurement Bias	Features or labels systematically vary across groups	Validate measurements across demographics; use multiple proxy measures
Aggregation Bias	Single model fails to account for group differences	Develop subgroup-specific models or calibration; disaggregated evaluation
Evaluation Bias	Testing data doesn't reflect deployment population	Ensure test sets are representative; conduct subgroup performance analysis

Fairness Metrics to Track

Demographic Parity: Equal selection/approval rates across groups
Equalized Odds: Equal true positive and false positive rates across groups
Predictive Parity: Equal positive predictive value across groups
Individual Fairness: Similar individuals receive similar outcomes
Counterfactual Fairness: Outcomes would be the same if protected attributes differed

⚖️ Fairness Trade-offs

Different fairness metrics can be mathematically incompatible (the "impossibility theorem"). Organizations must make explicit choices about which fairness criteria to prioritize based on use case context, stakeholder input, and legal requirements. Document these decisions and their rationale.

1.2.2 Reliability & Safety

Principle: AI systems must perform consistently and dependably within their intended operating conditions, while minimizing the potential for harm to individuals, organizations, and society.

Reliability Requirements

Robustness: Systems maintain performance across expected input variations and edge cases
Consistency: Similar inputs produce similar outputs over time
Graceful Degradation: Systems fail safely when encountering unexpected conditions
Reproducibility: Results can be recreated given the same inputs and conditions
Resilience: Systems recover appropriately from failures and attacks

Safety Considerations by Risk Level

Risk Level	Safety Requirements	Testing Approach
Minimal	Basic quality assurance, standard testing	Unit tests, integration tests, user acceptance testing
Limited	Enhanced testing, monitoring, disclosure requirements	A/B testing, canary deployments, performance monitoring
High	Rigorous validation, human oversight, audit trails	Red teaming, adversarial testing, fairness audits, third-party review
Prohibited	System cannot be deployed	N/A

Safety Engineering Practices

Failure Mode Analysis

Systematically identify how the system could fail and the consequences of each failure mode. Design mitigations for high-severity scenarios.

Adversarial Testing

Test system behavior under hostile conditions including malicious inputs, data poisoning attempts, and model extraction attacks.

Boundary Definition

Clearly define operational boundaries and implement guardrails to prevent operation outside safe parameters.

Incident Response

Establish procedures for detecting, responding to, and learning from safety incidents when they occur.

1.2.3 Privacy & Security

Principle: AI systems must protect individual privacy rights and secure sensitive data throughout the AI lifecycle, from training data collection through model deployment and retirement.

Privacy-by-Design Requirements

Data Minimization: Collect and retain only data necessary for the stated purpose
Purpose Limitation: Use data only for specified, explicit, and legitimate purposes
Consent & Notice: Obtain appropriate consent and provide clear notice of AI-powered processing
Individual Rights: Enable data access, correction, deletion, and portability rights
Retention Limits: Delete data when no longer necessary; implement automated retention policies

Privacy-Preserving Techniques

Technique	Description	Use Cases
Differential Privacy	Add calibrated noise to protect individual records while preserving aggregate insights	Analytics, model training, data sharing
Federated Learning	Train models on decentralized data without centralizing raw information	Multi-party collaboration, mobile devices
Homomorphic Encryption	Perform computations on encrypted data without decryption	Cloud processing of sensitive data
Secure Multi-Party Computation	Multiple parties jointly compute a function without revealing inputs	Collaborative analytics, benchmarking
Synthetic Data	Generate artificial data preserving statistical properties without real records	Testing, development, external sharing

AI-Specific Security Concerns

Model Theft: Protect models from extraction through query-based attacks
Data Poisoning: Prevent manipulation of training data to compromise model behavior
Adversarial Inputs: Detect and filter inputs designed to cause misclassification
Prompt Injection: Guard against malicious instructions embedded in LLM inputs
Membership Inference: Prevent attackers from determining if specific data was used in training
Model Memorization: Mitigate unintentional reproduction of training data in outputs

1.2.4 Transparency & Explainability

Principle: AI systems must operate in a manner that enables stakeholders to understand how decisions are made, with appropriate levels of transparency for different audiences.

Levels of Transparency

Audience	Information Needed	Delivery Mechanism
End Users	That AI is being used; general purpose; how to contest decisions	Clear notices, disclosure statements, help resources
Affected Individuals	Factors considered; how to seek human review	Explanation interfaces, appeal processes
Business Stakeholders	System purpose, limitations, performance metrics	Model cards, system documentation, dashboards
Regulators	Technical details, training data, validation results	Conformity assessments, audit logs, technical documentation
Developers	Architecture, code, hyperparameters, reproduction steps	Model cards, code repositories, experiment logs

Explainability Techniques

SHAP Values

Attribute predictions to features using game-theoretic approach. Provides global and local explanations for any model type.

LIME

Generate local interpretable explanations by approximating complex models with simpler ones around specific predictions.

Attention Visualization

For neural networks, visualize which inputs the model "attended to" when making predictions.

Counterfactual Explanations

Show what would need to change for a different outcome: "If X were Y, the decision would have been Z."

EU AI Act Transparency Requirements

AI System Disclosure: Users must be informed when interacting with AI (chatbots, emotion recognition)
Deep Fake Labeling: AI-generated or manipulated content must be disclosed
High-Risk Documentation: Detailed technical documentation, instructions for use, and audit logs
GPAI Transparency: Training data summaries, model capabilities and limitations, downstream provider information

1.2.5 Accountability & Human Oversight

Principle: Clear responsibility must be assigned for AI systems and their outcomes, with appropriate mechanisms for human oversight, intervention, and redress.

Accountability Framework

Level	Responsible Party	Accountabilities
Strategic	Board of Directors / C-Suite	Setting AI ethics policy, risk appetite, resource allocation
Tactical	AI Ethics Board / CAIO	Framework implementation, cross-functional coordination, escalations
Operational	Model Owners / Product Managers	Individual system compliance, risk assessments, documentation
Technical	Data Scientists / Engineers	Implementation quality, testing, monitoring, technical safeguards

Human Oversight Models

Human-in-the-Loop (HITL)

Human approval required for every AI decision. Appropriate for highest-risk decisions (medical diagnosis, criminal sentencing).

Human-on-the-Loop (HOTL)

Humans monitor AI decisions in real-time with ability to intervene. Suitable for time-sensitive decisions with moderate risk.

Human-over-the-Loop

Periodic human review and system tuning without real-time involvement. Appropriate for lower-risk, high-volume decisions.

Essential Accountability Mechanisms

Designated Model Owner with documented responsibilities for each AI system
Audit trails capturing all significant decisions and model changes
Clear escalation pathways for ethical concerns and incidents
"Stop the line" authority enabling any team member to halt deployment
Redress mechanisms for individuals harmed by AI decisions
Regular reporting to leadership on AI governance metrics
Third-party audits for high-risk systems
Insurance coverage for AI-related liabilities

Implementation Steps

Draft Your Ethical Principles Statement

Customize these five principles with organization-specific context, examples, and commitments. Ensure executive sign-off and Board endorsement.

Timeline: 2-3 weeks | Owner: CAIO / Legal / Ethics Board

Translate Principles to Policies

Develop operational policies that implement each principle. Include specific requirements, thresholds, and approval processes.

Timeline: 4-6 weeks | Owner: AI Governance Team

Create Assessment Criteria

Define how systems will be evaluated against each principle. Establish metrics, testing requirements, and acceptance thresholds.

Timeline: 3-4 weeks | Owner: Data Science / Quality Assurance

Integrate into Development Lifecycle

Embed principles checkpoints into your AI development process—from ideation through deployment. Create templates and checklists.

Timeline: 4-6 weeks | Owner: Development Teams / PMO

Communicate and Train

Launch organization-wide communication of principles. Develop role-specific training modules for developers, executives, and end users.

Timeline: 4-8 weeks | Owner: HR / Communications / Training

✅ Principles in Action

These principles should not remain abstract ideals. Each AI project should document how it addresses each principle, with specific evidence and testing results. This documentation forms the foundation of your compliance posture and audit readiness.