Some businesses have had to scramble as the pandemic destroyed or weakened their AI and ML models. This also underlined the need for governance to promote machine learning model accountability and traceability. A properly evaluated model governance system will reduce risks and raise the likelihood of successful models that meet business objectives.
The COVID crisis has wreaked havoc on carefully constructed AI models of many businesses. AI and ML models have become faulty or ineffective as a result of so many diverse variables shifting simultaneously. It’s crucial to have comprehensive documentation showing a model’s lifecycle, but that alone isn’t enough information once the model becomes unreliable.
Improved AI model governance is needed, which can help bring better accountability and traceability to AI/ML models by having practitioners answer questions like: Which input variables enter the model? What are the variables that will be output? In terms of particular metrics, how does the model behave? What role does AI model governance play in addressing these issues?
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An excessive amount of manual effort is required
To create their models, data scientists employ a range of tools. There are a lot of options to pick from because machine learning is still in its infancy. Some use cases, for example, are simply more effective with certain languages or frameworks, and data scientists have a strong preference for one language over another.
Because data scientists are so few and this field is so specialized, their work is siloed from the rest of the company. This makes it difficult for the oversight authority or the primary IT to ensure proper model governance and auditing across the company. That means this body will have to do a significant amount of manual work to visit all of the various departments and obtain the necessary model governance data. They can solve this problem by putting in place an AI governance system.
Taking a look at AI governance solutions
ML models must adhere to certain rules, expectations, and assumptions during the development process. When these models are put into production, they can produce very different results than when they are tested in a controlled environment. Governance is crucial in this situation.
Those in charge of governance must be able to keep track of the various models and versions that go along with them. To be effective, an AI governance solution’s catalogue must be able to track and document the framework in which the models are built.
As more enterprises have operationalized machine learning models in recent years, its dark side has arisen in the form of biases and other difficulties.
The ability to compute and track metrics that may impact these models, such as risks, anomalies, biases, levels of performance, and data drifts, is required. It’s not possible to calculate them in a lab; calculations must be done while the models are being manufactured.
It’s critical to have a dashboard that can provide these metrics to data scientists and business users. The metrics must be presented in a way that alerts business users to potential problems. And data scientists must be able to identify the metrics that will lead them to those potential problems. There should also be a feature that helps discover potential abnormalities based on the business-specific standards that have been defined — and that notifies both parties if anything isn’t right without bombarding them with false alarms.
Secure access is required for machine learning models
Model security is critical, especially in larger enterprises. If a model is mistakenly exposed to the wrong department, serious difficulties can arise. It is feasible to tweak and reverse-engineer models, but if one does not understand the environment in which they were created, their organization may be vulnerable to iteration processes. The altered models aren’t performing what they’re supposed to be doing in this scenario. The building where model governance was once housed has now been demolished.
Access to sensitive models that should not be shared with other departments should be restricted. Encryption and an audit trail are required to ensure that no unauthorized parties, including applications, have access to the model. It is necessary to establish a method that ensures transparency, traceability, and accountability.
Standardization is essential
Although there are numerous advantages to using a model governance solution, implementation can be difficult. The complex review flows of AI model governance are likely to impact effectiveness, speed, and cost.
Consistency is a major issue. The governance solution should be relevant to all business models, not just select departments. Because not all solutions provide standardization, this should be considered while evaluating them.