The 12 key principles of AI transparency

Key Principles of AI Transparency

Definition of AI transparency

AI transparency refers to creating visibility and openness around how AI systems are designed, operate, and make decisions.

Importance of AI transparency

Transparency in AI systems provides clear explanations for their operations, fostering trust, safety, compliance, and innovation.

Challenges of opaque AI systems

• Unreliable outputs
• Lack of standards
• Public distrust

Levels of disclosure

Organizations should provide visibility into data, algorithmic, interaction, and social impacts of AI systems.

CLeAR Documentation Framework

Aims to make AI systems comprehensible, trustworthy, and compliant through standardized transparency documentation.

Standardize transparency documentation across models

Using uniform formats to document key details about AI models allows for consistent evaluation by regulators, users, and researchers.

Proactive AI customer support

Launching AI agents for enhanced customer support.

Legible AI systems

Transparency involves explaining how and why AI models make decisions to promote understanding and trust.

Guidance for users

Help users understand how to apply, question, or reject model outputs, and define boundaries of model competence and appropriate use cases.

Omnichannel AI support

Leveraging AI across multiple channels for customer support.

Best practices with the CLeAR Framework

Utilize tools like model cards, datasheets for datasets, and transparency reports for enhanced transparency in AI systems.