Core Principles
⚖️
Transparency
Implementing audit trails and explainability mechanisms to make AI decision-making processes legible to stakeholders.
🛡️
Accountability
Establishing clear responsibility frameworks where human oversight ensures AI systems act within ethical boundaries.
🌍
Fairness
Applying bias-mitigation techniques during model training to ensure equitable outcomes across demographics.
🔒
Privacy
Implementing differential privacy and strict data governance to protect user information.
Challenges in AI Governance
Modern AI systems face complex ethical dilemmas such as:
- Algorithmic bias in hiring and lending systems
- Autonomous weapons decision-making
- Deepfake proliferation and disinformation
- Biometric data privacy
- Job displacement mitigation
Our research team investigates these challenges through a multidisciplinary approach combining ethics, law, and computer science.