Insights

The Imperative for Explainable AI in AIOps

Milgy George (Product Manager - AIOps)

In the dynamic world of IT operations, AIOps (Artificial Intelligence for IT Operations) has emerged as a transformative force, empowering organizations to proactively manage their digital landscapes. As someone who has been knee-deep in AIOps implementations for enterprises, I've witnessed first-hand the incredible potential of AIOps. However, with great power comes great responsibility, and in this blog post, we'll explore why the need for explainable AI in AIOps solutions is more critical than ever.

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A new era of ITOps : Less drama, more data

AIOps systems, fuelled by the mighty forces of AI and machine learning, crunch colossal amounts of data without breaking a sweat. They spot anomalies, predict incidents before they happen, and even automate responses, all at a scale and speed that humans alone cannot match.

However, as AIOps systems become more sophisticated, they also become more opaque. The inner workings of complex AI algorithms can be akin to a black box, leaving IT professionals wondering:

• Why did AI make a specific recommendation or decision?

• How does the AI differentiate between normal and anomalous behavior?

• Can we trust the AI's conclusions when incidents occur?

When we can't see how things work, it makes some people doubtful, cautious, and sometimes reluctant to fully use AIOps solutions. To deal with these worries and get the most out of AIOps, we need to make sure explainable AI (XAI) is a key part of the picture.

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Decoding explainable AI

Explainable AI (XAI) refers to the capability of an AI system to provide clear, understandable, and interpretable explanations for its decisions and actions. In AIOps, this helps IT teams understand and trust the advice AIOps tools provide. Here's why it's crucial.

Trust and Accountability: XAI fosters trust in AI-driven decisions. IT professionals can verify that the AI's recommendations align with their expertise and goals, ensuring accountability in the decision-making process.

Efficient Incident Resolution: When an incident occurs, having insights into why the AI flagged it as critical allows IT teams to respond faster and with greater confidence. They can also make more informed decisions about resolution strategies.

Continuous Improvement: XAI provides valuable feedback loops. IT operations teams can analyze AI-generated explanations to refine their understanding of system behavior, improve incident response, and enhance overall system performance.

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Implementing explainable AI in UST SmartOps AIOps

As a product manager specializing in AIOps, I believe that incorporating XAI into AIOps solutions is not just a feature; it's a fundamental requirement. Here's how we achieve this in UST SmartOps AIOps:

Interpretable Models: Develop AI models that are inherently interpretable, allowing users to understand how decisions are made. SmartOps uses ML algorithms to correlate alerts and create clusters out of them. Similar cluster alerts will have similarity either on text or the pattern which they occur. It is important for the ITOps Engineer to know the logic behind clustering these alerts together. Alerts correlations using text similarity can effortlessly be explained to an ITOps engineer by demonstrating the similarity calculation. This helps the engineer to evaluate the alert correlations.

Transparency Tools: Provide user-friendly interfaces that display AI-generated explanations alongside recommendations and alerts. The Critical Event Prediction UI in UST SmartOps does this very effectively. The predictions are based on the alert / incident patterns which are mined from historic data using pattern mining algorithms. For instance, if alerts A, B, C leads to a critical event D forms a pattern; then this information is also shared along with the future event prediction for critical event D. Showing the user the explanation along with the prediction fosters trust.

Educational Resources: Offer training and documentation to help IT teams leverage XAI effectively, ensuring they understand and trust the AI's insights.

While AIOps holds incredible promise for transforming IT operations, the need for explainable AI cannot be overstated. It bridges the gap between advanced AI capabilities and human understanding, enabling organizations to fully embrace and leverage AIOps solutions like UST SmartOps AIOps.

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