AI and Compliance- Transparency and Explainability

Model Explainability  

How will the AI model's decision-making process be documented and made understandable to users?

The AI system’s decision-making process will be documented through detailed technical documentation. This documentation will include explanations of the algorithms used, the data preprocessing steps, the criteria for summarization, and the labeling methodology. Additionally, we will provide visual aids and examples to illustrate how the model processes input data to generate summaries and labels. User interfaces will include tooltips and help sections to explain key features and functions in an accessible manner.

Auditability

What mechanisms are in place for auditing the AI module’s outputs and ensuring they align with expected standards?

Regular audits will be conducted to evaluate the outputs of the AI module. These audits will involve:

• Internal Reviews: Periodic internal reviews by our technical team to assess the accuracy and relevance of the summaries and labels.

• External Audits: Engagement with independent third-party auditors to review and validate the module's performance and compliance with standards.

• Quality Control Checks:Implementation of automated quality control checks that flag anomalies or deviations from expected outputs.

• Reporting and Logging: Maintenance of detailed logs and reports on the module's outputs, enabling traceability and accountability.