How exactly does AI model governance help tackle these issues? And how can you ensure you’re using it to best fit your needs? Read on.
The pandemic has wreaked havoc on the carefully developed AI models many organizations had in place. With so many different variables shifting at the same time, what we’ve seen in many companies is that their models became unreliable or useless. Having good documentation showing the lifecycle of a model is important, but that still doesn’t provide enough information to go on once a model becomes unreliable.
What’s needed is improved AI model governance, which can help bring greater accountability and traceability for AI/ML models by having practitioners address questions such as:
Has any unauthorized person gained access to it?
How exactly does AI model governance help tackle these issues? And how can you ensure you’re using it to best fit your needs? Read on.
Data scientists use a variety of tools to develop their models, whether it’s SAS, R, Python or the multitude of machine learning software libraries available today. With machine learning still in its nascency, there are a ton of options to choose from. Some use cases are simply more effective with certain languages or frameworks, for example, and data scientists tend to be relatively loyal to one language over another.
Since this field is so specialized and data scientists are so few, their work is siloed from the rest of the enterprise. This makes it difficult for the primary IT or oversight body to guarantee appropriate company-wide governance and audit of models. That means this body will need to exert major manual effort to go to all the various departments and gather the needed model governance information. They can overcome this issue by implementing an AI governance solution.
There are certain expectations, rules and assumptions that ML models must abide by during the development process. When these models are deployed into production, they can yield quite different results from those in controlled development environments. Governance is critical here.
Those involved in governance must have a means of tracking the different models and the different versions associated with the models. For an AI governance solution to be effective, its catalog must have the ability to track and document the framework that the models are developed in.
In addition, the catalog must have the ability to ensure model lineage where it associates the models with the functionality features within the models. Importantly, it enables computation of the appropriate governance metrics of the various features.
In recent years, as more organizations have operationalized ML models, their dark side has emerged in the form of biases and other issues. An example would be a financial institution whose models recommend offering lower credit limits to women compared to men living in the same home.
