What are the 12 key questions to ask during an AI vendor audit?

What is an AI vendor audit?

An AI vendor audit is a thorough evaluation of an artificial intelligence (AI) vendor's capabilities, products, and services to ensure they meet your organization's needs and requirements. It's a critical step in the procurement process, as it helps you make an informed decision about whether to partner with the vendor.

Here are 12 key questions to ask during an AI vendor audit:

1. What is your AI technology stack?

Ask the vendor to describe their AI technology stack, including the types of algorithms, models, and tools they use. This will help you understand their capabilities and whether they align with your organization's needs.

2. What is your data processing and storage approach?

Find out how the vendor processes and stores data, including their data centers, cloud infrastructure, and data security measures. This is crucial for ensuring data privacy and compliance.

3. How do you handle data quality and preprocessing?

Ask the vendor about their data quality and preprocessing processes, including how they handle missing values, outliers, and data normalization. This will help you understand their ability to work with complex data sets.

4. What is your approach to model explainability and transparency?

Find out how the vendor approaches model explainability and transparency, including their methods for interpreting and visualizing AI models. This is essential for building trust and understanding in AI decision-making.

5. How do you handle data bias and fairness?

Ask the vendor about their approach to handling data bias and fairness, including their methods for detecting and mitigating bias in AI models. This is critical for ensuring AI systems are fair and unbiased.

6. What is your approach to model validation and testing?

Find out how the vendor approaches model validation and testing, including their methods for evaluating model performance and identifying potential issues. This will help you understand their commitment to quality and reliability.

7. How do you handle model updates and maintenance?

Ask the vendor about their approach to model updates and maintenance, including their methods for updating and refining AI models over time. This is essential for ensuring AI systems remain accurate and effective.

8. What is your approach to data governance and compliance?

Find out how the vendor approaches data governance and compliance, including their methods for ensuring data privacy, security, and regulatory compliance. This is critical for protecting sensitive data and avoiding legal issues.

9. How do you handle data breaches and incidents?

Ask the vendor about their approach to handling data breaches and incidents, including their methods for detecting and responding to data security incidents. This will help you understand their commitment to data security and incident response.

10. What is your approach to AI ethics and responsible AI?

Find out how the vendor approaches AI ethics and responsible AI, including their methods for ensuring AI systems are developed and used in an ethical and responsible manner. This is essential for building trust and understanding in AI decision-making.

11. How do you handle AI model interpretability and debugging?

Ask the vendor about their approach to AI model interpretability and debugging, including their methods for understanding and troubleshooting AI model behavior. This will help you understand their ability to work with complex AI systems.

12. What is your approach to AI vendor management and support?

Find out how the vendor approaches AI vendor management and support, including their methods for providing training, documentation, and ongoing support to customers. This is essential for ensuring a successful partnership and minimizing downtime.