How can you ensure AI models are scalable for data governance?

Your industry and existing governance processes significantly should influence the approach to govern AI models. Actually, AI models shouldn't be treated as exceptions but rather subjected to the same governance standards. It's crucial to note that AI, due to its propensity for bias, demands an additional focus on ethics and compliance within governance. In practice, what I often observe is that many companies are incorporating additional layers atop their AI services to monitor and identify bias, as well as to detect unexpected or undesirable behavior in the outputs. This proactive stance not only ensures adherence to ethical standards but also promotes a responsible and accountable use of AI technologies.

Scaling isn't the primary concern; many companies embrace MLOps, use containers, and microservices for AI. The challenge is maximizing ROI and minimizing TCO. Prioritize simple algorithms for routine tasks and advanced ones for complex ones. Leverage proven systems for scalability instead of reinventing the wheel in AI deployment and monitoring.

Transparency is the main word. All your stakeholders and users need to understand clearly that they’re consuming AI as the end product, while knowing fully the ingredients and recipe used and be signed up to it. If not, the questions on data quality, bias and clarity will grow while use of the AI product will fall.

In the different stages of the AI model life cycle, which include development, training, validation, and implementation, specific roles hold immense importance. The Model Owner, Model Manager, and Validator are among these roles that require special attention. To establish clear accountability and responsibility, it is highly recommended to utilize a RACI matrix.

A Model inventory that provides detailed descriptions of each model, including specific characteristics such as their intended purpose, parameters, training methods, potential applications, performance metrics, utilization, implications of bias, associated risks, and inherent limitations. This inventory is particularly useful as it lists the type and source of inputs used by each model, as well as its outputs and intended use. Additionally, it indicates whether the models are functioning properly and provides information on when they were last updated. To ensure the effective use of this inventory, it is essential to gather and comprehend this information, alongside utilizing data documentation and communication tools.