Privacy & Responsible AI
Privacy Protection & Ethical AI Development
Build trustworthy and secure AI systems with ethical principles
personal data protection
ethical AI development
trust and transparency
Privacy Law Compliance
PDPA Thailand
6 Legal Bases for Processing
- • Consent
- • Contract
- • Legal Obligation
- • Vital Interest
- • Public Task
- • Legitimate Interest
Data Subject Rights
- • Right of Access
- • Right to Rectification
- • Right to Erasure
- • Right to Object
GDPR Europe
Core Principles
- • Privacy by Design
- • Privacy by Default
- • Data Minimization
- • Purpose Limitation
Impact Assessment
- • DPIA Process
- • Risk Assessment
- • Safeguarding Measures
- • Reporting Requirements
Responsible AI Principles
Core Principles
Fairness & Non-discrimination
AI systems must treat all individuals equitably
Transparency & Explainability
AI decisions must be interpretable and explainable
Accountability & Governance
Clear governance and responsibility structures
Privacy & Security
Protect personal data and ensure security
Implementation Practices
Bias Detection & Mitigation
Detect and reduce bias in data and models
Model Interpretability
Use XAI techniques to explain decisions
Human-in-the-Loop
Integrate human decision-making in AI systems
Continuous Monitoring
Continuously monitor and evaluate AI systems
Technical Implementation
Privacy Techniques
- Differential Privacy
- Federated Learning
- Homomorphic Encryption
- Secure Multi-party Computation
Fairness Tools
- Fairlearn (Microsoft)
- AIF360 (IBM)
- What-If Tool (Google)
- Aequitas
Explainability
- LIME
- SHAP
- Captum (PyTorch)
- InterpretML
Governance
- Model Cards
- Data Sheets
- AI Ethics Boards
- Audit Trails
Ready to Build Ethical AI?
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