Definition
Transparency ensures that stakeholders can understand how an AI model arrives at its outputs, fostering trust and accountability. It involves disclosing training data origins, model architectures, and potential biases. In ethical AI frameworks, transparency complements explainability by making system behaviors predictable and auditable, allowing users to verify fairness and identify errors without requiring deep technical expertise.
Summary
The degree to which an AI system’s decision-making processes, data sources, and limitations are open and understandable to users.
Key Concepts
- Explainability
- Accountability
- Bias Disclosure
- User Trust
Use Cases
- Regulatory compliance reporting
- Building user trust in healthcare AI
- Auditing algorithmic decisions