Is Your Organization Ready To Implement AI Model Governance?

Know more about AI model governance and its important aspects.

AI model governance

AI model governance

The process by which an organization controls, manages access rights, executes policy, and tracks activity for models is what is known as AI model governance. It is the base of an organization to curtail risks involved in its production. Model governance is essential to reduce organizational risk in the event of an audit but involves a lot more than just intended action. 

Organizations that effectively execute the components of AI model governance can attain a proper level of control and clearance about how models work in production while unlatching operational efficiencies that help in achieving something more than AI investments. Organizations can easily track, determine, monitor, and control the approaches to all models with the help of AI model governance. 

The implementation of AI model governance not only benefits an organization with a high level of visibility to quickly recognize and alleviate the potential risks of AI but also helps in maximizing the performance of models in production. 

Good documentation showing model lifecycle helps but it is untrustworthy when those models become unstable. In such cases, AI model governance helps in bringing accountability and credibility to artificial intelligence and machine learning models.

Following aspects to keep in mind while implementing AI governance solutions to model. 


AI Model Governance  

Data scientists use different tools to create their models like some use R, Python and some use SAS. This creates a challenge for the central IT or governing organization to secure proper governance and audit of those models across the organization. The government body has to make an effort to assemble all the model’s information. In such cases, AI governance solutions assist the central body to perform the task efficiently and effectively at a fast rate of speed. 


Abilities of AI Governance Solution 

While developing AI and ML models, there are certain assumptions, rules, and regulations that direct the process of development. Once the models are implemented in the production, the real-world production results can differ from that of controlled development environment results. This is the point where governance becomes a critical task. There are certain ways to track various models and versions that are associated with those models. In an AI governance solution, the catalog needs to be able to keep track and record the framework where the models are developed. The catalog also needs to confirm the origin from where the models get associated with the functionality features.


Calculate the Performance  

It is required to calculate and track metrics like biases, risks, levels of performance, data drifts, and deviations that might affect the models. But this can’t be done in the lab environment. These matrices are needed to be computed when the models are in production. 

Dashboards should emphasize these metrics and serve both the business users and data scientists. This dashboard should inform the business users and provide direction to the data scientists on where to address future problems. There is also a need for a mechanism that enables setting business-specific approaches and discovering potential oddities to notify the business users and data scientists.


Security Challenges

In large enterprises security of models is the foremost task because if a model gets revealed accidentally to the wrong department chaos can emerge. Models can be twisted and adjusted but when such things are done without comprehending the original context, it brings potential threats that can put the enterprise at risk. When there are critical models that can’t be shared with other departments, it becomes important for the organization to secure access rights to those models.


Implementing Model Governance

There is no doubt that AI model governance benefits an organization but along with it, it is to be noted that the implementation of AI governance solutions is not easy and smooth. It involves critical review structures that can hinder the speed and effectiveness of production. A government solution must be consistent that can be implemented on all models and not just specific models only. 

AI model governance solutions and strategies work across various departments throughout the entire organization helping in systematic processes and simplified governance. Therefore it is important to put a governance structure in place to benefit from it. 


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