AI lifecycle monitoring

In an era where Artificial Intelligence (AI) is becoming the backbone of many enterprises, its integrity is as crucial as its innovation. AI lifecycle monitoring is like a sentinel, overseeing the journey of AI models from inception to retirement. Once an AI model is deployed, monitoring its performance is crucial to ensure it meets the intended business goals and performs reliably. This ongoing check helps build trust in AI technology. We’ll delve into various facets of AI lifecycle monitoring—touching upon its role in ensuring compliance, fostering explainability, fairness, model drift monitoring, retraining, risk management, and eventually linking AI performance to the enterprise’s goals.

Understanding AI decisions

AI is often seen as a complex ‘black box’ where its decisions are hard to understand. This can be risky financially and for a company’s reputation, especially if the AI has biases. Making the AI’s decisions clear and understandable to everyone is essential to build trust.

Tools for clarity: Use tools like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to better understand how the AI model makes decisions. For example, using a tool like SHAP to analyse a loan-approval AI model can help explain why certain applicants were approved or denied loans based on various features like their income, credit score, and employment status.
Simple algorithms: Use easy-to-understand algorithms where appropriate, like decision trees or linear regression, to make AI decisions clear. For example, a small online retail platform could use a decision tree for a recommendation system instead of a complex neural network to simplify understanding of how they make product recommendations. Don’t overcook things.
Well-documented models: Keep thorough documentation of the models, including the features used and the logic behind decisions, to provide insights into how the AI works. For example, keeping comprehensive documentation for a fraud detection model explaining the features and reasoning can help stakeholders understand how it flags suspicious activities.

Finding and fixing bias by implementing AI lifecycle monitoring

It’s essential to regularly check AI models for biases and correct them to ensure fairness in the AI’s actions.

Detecting biases: Use tools and frameworks to find and measure biases in the AI models. For example, you could use a fairness tool to analyse a hiring AI model to ensure it doesn’t favour any particular gender or ethnicity.
Correcting biases: Correct identified biases to ensure the models are fair and unbiased. For example, adjusting the hiring model to correct detected biases, ensuring a level playing field for all candidates.
Ongoing checks: Keep checking for biases to maintain fairness over time. For example, setting up regular bias audits for the hiring model to maintain fairness over time.

Handling changes in model performance

Over time, AI models may start to perform differently as the data changes. Monitoring this ‘model drift’ and taking corrective action when needed is crucial.

Regular monitoring: Continuously check the model’s performance to identify any changes. For example, tracking the accuracy of a sales forecasting model daily to spot any unusual deviations.
Understanding changes: Analyse any changes to find out why they happened and how serious they are. For example, investigating if a sudden drop in prediction accuracy was due to a change in consumer behaviour or an error in the model.
Getting alerts: Set up automated alerts to notify you as soon as any drift is detected. For example, setting up automatic alerts to inform the team whenever the sales forecasting model’s accuracy falls below a certain threshold.
Correcting drift: Take steps to correct the drift, like updating data or retraining the model. For example, retraining the sales forecasting model with fresh data to realign its predictions with the current market conditions.

Updating AI models through AI lifecycle monitoring

As the data landscape evolves, it’s vital to retrain AI models to ensure they remain accurate and compliant with new requirements.

Collecting new data: Gather updated and relevant data to reflect current conditions. For example, collecting new customer feedback data to update a customer satisfaction prediction model.
Evaluating performance: Check the model’s performance with the new data to find areas for improvement. For example, assessing the updated model to ensure it now predicts satisfaction levels more accurately.
Retraining: Use the new data to improve the model’s accuracy. For example, retraining a product recommendation model with new sales data to reflect current buying trends.
Validation and redeployment: Validate the retrained model and deploy it again, ensuring it performs well post-deployment. For example, validating the retrained recommendation model against a holdout dataset before redeploying it to ensure it performs as expected.