Explainable AI

Explainable AI techniques at enterprise scale

The Fiddler AI Observability platform delivers the best interpretability methods available by combining top explainable AI principles, including Shapley Values and Integrated Gradients, with proprietary explainable AI methods. Obtain fast model explanations and understand your ML model predictions quickly with Fiddler Shap, an explainable method born from our award-winning AI research.

Hear from our Data Science team about different Explainable AI concepts:

Gain visibility into your most deeply complex models

To ensure continuous transparency, Fiddler automates documentation of explainable AI projects and delivers prediction explanations for future model governance and review requirements. You’ll always know what’s in your training data, models, or production inferences.

• Understand every single prediction made by your AI solution and spot discrepancies using out-of-the-box explanation methods.
• Simulate input scenarios in tabular and text models to validate and build trust.
• Bring in data and models from any platform and gain faithful explanations using the best explainable AI methods available.

Explain your models with Fiddler

Increase coverage, efficiency, and confidence in your models

You can deploy AI governance and model risk management processes effectively with Fiddler. 

Not only do you save money from potential fines and penalties, but the likelihood of any negative publicity is reduced when you can detect and resolve issues or deep-rooted model bias before it is exposed.

• Understand model predictions at a global and local level before it’s put into production.
• Generate any type of report required, including compliance reports.
• Create and share custom reports for MRM and compliance reviews using Fiddler Report Generator

Minimize model risks and stay compliant

Responsible AI principles make customers happy

It’s important to understand model performance and behavior before putting anything into production, which requires complete context and visibility into model behaviors — from training to production.

When areas of low performance or potential issues are rooted out before they are seen, the customer experience is improved, leading to higher Net Promoter Scores (NPS) and stronger customer recommendations. 

• Compare distributions across training, test, and production data.
• Explain performance discrepancies across groups by slicing data into groups.
• Find anomalies and data drift by analyzing AI predictions in relation to an entire data set or specific groups.

Delight your customers with Fiddler