Model Explainability: Can users understand how the AI arrives at its outputs? Are the factors and logic behind decisions accessible?

Data Transparency: Is it clear what data was used to train and operate the AI? Are data sources, quality, and limitations disclosed?

Process Transparency: Are the design, deployment, and update processes for the AI system documented and open to review?

User Communication: Are users informed when they are interacting with AI, and do they know how to seek explanations or recourse?

Use interpretable models where possible, or provide post-hoc explanations for complex models (e.g., LIME, SHAP).

Document model architecture, training data, and decision logic in model cards or datasheets.

Offer clear user interfaces for requesting explanations or reviewing AI decisions.

Engage in regular audits and open reporting of system performance and limitations.

Balancing transparency with intellectual property, security, or privacy concerns.

Explaining complex or deep learning models in ways that are meaningful to non-experts.

Ensuring explanations are accurate, actionable, and not misleading.