10 points checklist to perform an audit of AI/ML systems
How to do an audit of AI systems? What should be the scope of the audit of AI systems? What are the advantages of audit of AI systems? These are the questions asked by enterprises as AI grows and inculcates into the big IT systems rapidly.
This blog list out 10 points checklist to perform an audit of AI systems.
| Roles and responsibilities defined by the auditee organization | A list of roles and responsibilities should be defined in the auditee organization to implement the IT system. Examples of some roles include project leader to manage project activities, developer to develop AI/ML models, one role related to seeing the aspects of privacy, etc. |
| Hardware and Software Requirements | Hardware and Software are crucial in running AI/ML systems. Adequacy of hardware ensures proper processing power to run systems. Software adequacy includes checking genuine licenses, patch updation on time, etc. Ensure the organization uses existing frameworks and regulations to implement AI systems. |
| Algorithms used | Must ensure algorithms used for collecting data is close to real data. Check the data acquisition method, data quality of raw data, the database structure, etc., and ensure the correctness of data at multiple levels. In summary, the usage of validated algorithms for different purposes must be ensured for the correctness of AI/ML systems |
| Data Sources | Must ensure data used in the system is close to real data. Check the data acquisition method, data quality of raw data, the structure of the database, etc., and ensure the correctness of data at multiple levels. |
| Data Privacy | Data privacy is a critical factor and must be enforced by any organization. Ensure data privacy by way of checking the processes involved in handling data. |
| Data Cleaning | Clean data is most important in getting less inaccurate results. Ensure the proper mechanism is in place to remove data that is not relevant to the AI/ML system. |
| Maintenance | Ensure proper maintenance of the AI/ML system by verifying the process involved in the day-to-day activities. |
| Evaluation | Algorithms are the backbone of AI systems. Here, we must check the algorithms that should be brought from trusted libraries. If an organization implements AI/ML algorithms, a subject matter expert (SME) must perform a thorough audit. |
| Security Risks | Ensure security is considered in each use case of the AI system. Face recognition used for authentication should not allow adversaries wrongly. Another aspect is to manage the explosion of data from edge computing and IoT, which must be securely transferred and processed between different endpoints. Must ensure code review also infrastructure security of the AI/ML systems. |
| Compliance and Governance | Ensure implementation of data protection laws applicable in the country (e.g. GDPR, CCPA, etc.). It is recommended for periodic assessment of AI/ML systems for identifying any issue with compliances. |
Conclusion
Every enterprise must follow international standards while implementing AI systems. In addition, audits of those systems uncovered many new issues from AI systems. Auditors must devise a plan for audits and define success and failure criteria for the audit. Here, technical documentation of the AI system played a critical role.
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Disclaimer: This tutorial is for educational purpose only. Individual is solely responsible for any illegal act.
