AI audit checklist for new systems

Data Collection and Processing

Data Collection

Is the data collection process compliant with relevant privacy regulations?

Is informed consent obtained from individuals whose data is being collected?

Are appropriate safeguards in place to protect sensitive information?

Is data anonymized or pseudonymized whenever possible?

Data Processing

Are there clear guidelines on how data is processed and for what purposes?

Is the data processing transparent and explainable?

Are there measures to ensure the accuracy and integrity of the data?

Is there a data retention policy in place?

Model Development and Training

Model Design

Is the model designed to avoid bias and discrimination?

Are there clear objectives for the model's performance and limitations?

Are there measures in place to ensure model interpretability?

Training Data

Is the training data representative and diverse?

Are there mechanisms to identify and mitigate biases in the training data?

Is the training data labeled accurately and consistently?

Is there a process to update and refine the training data?

Model Evaluation and Deployment

Model Evaluation

Is there a comprehensive evaluation plan in place?

Are there metrics to measure performance, fairness, and robustness?

Are there mechanisms to monitor the model's ongoing performance and accuracy?

Are there procedures to address and correct any identified issues?

Model Deployment

Are there safeguards to prevent unauthorized access or tampering?

Is there a mechanism for user feedback and complaint handling?

Are there procedures to handle any unintended consequences or harmful outputs?

Is the model periodically reevaluated and updated?

Ethical Considerations

Accountability and Governance

Is there a designated person or team responsible for the AI system's ethical use?

Is there a clear governance framework in place?

Are there mechanisms for external audits or reviews?

Is there a process for addressing ethical concerns and complaints?

Fairness and Non-Discrimination

Are there measures to identify and mitigate biases in the system's outcomes?

Is the system's impact on different demographic groups assessed?

Are there procedures to address any identified fairness issues?

Transparency and Explainability

Is there transparency about the system's capabilities and limitations?

Are there mechanisms to provide explanations for the system's outputs?

Is there an accessible documentation of the system's design and functioning?

Please note that this checklist serves as an example, and the specific items and sections may vary depending on the context and requirements of the AI system being audited.