Fairness and Non-Discrimination: Ensuring AI systems make unbiased decisions by addressing both explicit and implicit biases in training data.
Transparency and Explainability: Developing AI systems that are transparent and understandable, making it easier for users to comprehend how decisions are made.
Privacy and Data Protection: Implementing robust data privacy measures to protect user information and ensure compliance with regulations.
Accountability and Governance: Establishing clear accountability frameworks to oversee AI development and deployment, ensuring responsible use.
Ethical Data Sourcing: Using ethically sourced data to train AI models, avoiding biases and ensuring diverse representation.