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  • Writer's pictureBahar Ansari

an internal audit by AI for AI companies

Updated: Apr 12, 2023



An internal audit for AI companies should focus on evaluating the development and deployment of AI systems to ensure that they align with the company's values, goals, and regulatory requirements. Below is a model internal audit framework for AI companies to use:


1. Governance and Accountability

1.1 Evaluate the governance and accountability structure for AI systems, including the roles and responsibilities of developers, users, and regulators.

1.2 Assess the level of human oversight for AI systems to ensure safety and ethicality.

1.3 Review the communication and reporting mechanisms for AI systems to ensure transparency and accountability.


2. Risk Assessment

2.1 Evaluate the risk assessment process for AI systems to identify potential risks, including safety, privacy, and human rights.

2.2 Assess the adequacy of the mitigation measures developed to address identified risks.

2.3 Review the independent review process for risk assessments to ensure impartiality and accuracy.


3. Transparency and Explainability

3.1 Assess the level of transparency in the development, training, and performance of AI systems to ensure transparency.

3.2 Evaluate the explainability of AI systems to ensure that they provide explanations for their decisions and actions to users and stakeholders.

3.3 Review the communication mechanisms for AI systems to ensure that they are understandable and accessible to users and stakeholders.



4. Privacy and Security

4.1 Assess the level of data protection measures implemented, such as anonymization and encryption, to ensure privacy and security.

4.2 Evaluate the compliance of AI systems with personal data protection laws to ensure that they do not violate individuals' privacy.

4.3 Review the security measures implemented to prevent unauthorized access, use, or disclosure of data.


5. Fairness and Bias

5.1 Evaluate the testing process for AI systems to ensure fairness and identify and mitigate any identified biases.

5.2 Assess the compliance of AI systems with non-discrimination laws to ensure that they do not discriminate against individuals based on protected characteristics.


6. Training and Education

6.1 Assess the level of training and education provided to developers and users of AI systems to ensure that they have the necessary knowledge and skills to develop and use AI systems responsibly.

6.2 Review the effectiveness of the training and education programs implemented to ensure that they align with the company's values, goals, and regulatory requirements.


7. Enforcement and Penalties

7.1 Review the enforcement mechanisms implemented to ensure compliance with regulatory requirements and company policies.

7.2 Evaluate the penalties imposed for non-compliance to ensure that they are appropriate and effective in deterring non-compliant behavior.


8. Review and Revision

8.1 Evaluate the periodic review process implemented to ensure that the internal audit framework remains effective and appropriate.

8.2 Assess the effectiveness of the revision process implemented to address emerging risks and challenges associated with AI systems.


The internal audit should conclude with a report summarizing the findings of the audit and providing recommendations for improving the development and deployment of AI systems. The report should be presented to the company's executive team and board of directors for review and action.


Measuring and scoring the responses to an internal audit for AI companies can be done in several ways, depending on the specific criteria being evaluated. Here are some examples:

  1. Governance and Accountability: Responses can be measured based on the level of detail provided on the governance structure and accountability mechanisms for AI systems, with a score of 1 to 5, where 1 represents inadequate information and 5 represents a comprehensive and well-defined governance structure.

  2. Risk Assessment: Responses can be measured based on the comprehensiveness of the risk assessment process, with a score of 1 to 10, where 1 represents an incomplete risk assessment and 10 represents a thorough and comprehensive risk assessment process.

  3. Transparency and Explainability: Responses can be measured based on the level of detail provided on transparency and explainability measures for AI systems, with a score of 1 to 5, where 1 represents inadequate information and 5 represents a comprehensive and well-defined transparency and explainability framework.

  4. Privacy and Security: Responses can be measured based on the level of compliance with personal data protection laws and the adequacy of security measures, with a score of 1 to 10, where 1 represents non-compliance and inadequate security measures and 10 represents full compliance and comprehensive security measures.

  5. Fairness and Bias: Responses can be measured based on the level of detail provided on testing for fairness and bias, with a score of 1 to 5, where 1 represents inadequate testing and 5 represents a comprehensive and well-defined testing process.

  6. Training and Education: Responses can be measured based on the effectiveness of the training and education programs implemented, with a score of 1 to 10, where 1 represents ineffective training and education programs and 10 represents highly effective training and education programs.

  7. Enforcement and Penalties: Responses can be measured based on the adequacy of enforcement mechanisms and penalties imposed for non-compliance, with a score of 1 to 5, where 1 represents inadequate enforcement mechanisms and penalties and 5 represents comprehensive and effective enforcement mechanisms and penalties.

Overall, the responses can be scored on a scale of 1 to 100, with a higher score indicating better compliance and adherence to regulatory requirements and company policies. The score can be used to track progress over time and identify areas that need improvement.

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