Lessons for auditing AI, from avoiding model groupthink to revealing blind spots

Dec 9, 2023 ·

AI auditing is an important function of modern technology, ensuring that the AI models created by organizations are reliable and accurate. With the increasing complexity of AI models, it can be challenging to audit their performance. However, there are five key lessons for auditing that can help make this process easier and more effective.

The first lesson is the importance of avoiding so-called “model groupthink”. This phenomenon occurs when different models are created using similar data sets and parameters, which can lead to overly optimistic predictions about the accuracy of the results. By using different data sets and parameters, organizations can create models that are more robust to changes in the environment.

The second lesson is to ensure that all teams and stakeholders involved in the development of the AI model have a clear understanding of the objectives of the project. Without a unified goal, it can be difficult to accurately audit the performance of the model. Furthermore, teams should also consider the ethical implications of their model and the potential risks it may present to users before attempting to audit its performance.

The third lesson is to look beyond the surface when evaluating the performance of a model. It is important to investigate the inner workings of a model in order to gain a better understanding of how it functions. Auditors should be mindful of potential blind spots, such as biases in the data set used to train the model, which could lead to inaccurate results.

The fourth lesson is to ensure that all stakeholders have access to the same information in order to accurately assess the performance of the AI model. Teams should be sure to share all relevant information with all parties, including data scientists, developers, and decision makers, in order to create a common understanding of the model.

Finally, the fifth lesson is to conduct regular evaluations of the AI model. This will help identify any flaws in the model before it is implemented, thereby reducing the risk of errors or bias in the results. Regular evaluations will also help ensure that the model remains accurate and up-to-date over time.

Overall, these five key lessons for auditing AI models can help organizations to create reliable and accurate models that meet their objectives. By avoiding model groupthink, understanding the objectives of the project, looking beyond the surface, sharing information between stakeholders, and conducting regular evaluations, organizations can ensure that their AI models are functioning correctly and provide reliable results.