AI Model Auditing
This is a short guide on AI model auditing.
Auditing models for fairness and inclusion reduces risk and increases the quality of the model. When assessing models, there Are steps to take that will allow you to reduce the amount of bias in the model.
There are also many auditing tools, resources and best practices to consider. Auditing models and algorithms for bias is a proactive measure that helps to mitigate the risk of negative impact in results. When audits catch issues and the model is improved prior to implementation, higher quality results are produced.
When working with models, consistency in the quality of results, will reduce the amount of bias in the results and help to mitigate the bias that might appear in the model. When auditing the AI, the following steps can be followed to ensure that the model was thoroughly assessed for bias. When beginning the process, create clear objectives to align the purpose of the project with the outcomes.
Discuss the goals and purposes of the project with experts to gather other perspectives and ensure there are fewer oversights. Examine the components of the algorithm and determine whether there is a need to alter or adapt the program. Inspect the dataset and assess whether it is inclusive or if any groups are underrepresented.
Determine if there is any impact to populations, if any are underrepresented, or if the data is not reflective of the populations involved. As you make corrections and iterate the model, repeat and continue to improve. Implement the changes and continue to monitor the model. There are many different mechanisms that call for a model to be audited, or that align with the audit of an AI model. Human-in-the-loop practices maintain the importance of keeping a human interacting with and monitoring the model.
This lends well to auditing a model, as the human in the loop can consistently track the model. Aligning the goals and objectives of the program with ethical guidelines and maintaining legal compliances also create a need for a model to be audited. Assessing an algorithm for bias also poses the need to audit and adjust AI programs.