Know the why and the how behind your AI solutions

Model performance is reliant not only on metrics but also on how well a model can be explained when something eventually goes wrong. 

Explainable AI techniques at enterprise scale

Benefit from our award-winning AI research for understanding your ML model predictions. Fiddler delivers the best interpretability methods available by combining top explainable AI principles, including Shapley Values and Integrated Gradients, with proprietary explainable AI methods. 

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

Build trusted AI solutions that delight customers

Fiddler AI feature attribution
Deploy responsibly

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.
  • Simulate input scenarios in tabular and text models to validate and build trust.
  • Bring in data and models from any platform and benefit from the best explainable AI methods available.
Minimize risk

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 performance before it’s put into production. Discover areas of low performance, compare different models and slice and explain.
  • Generate any type of report required, including compliance reports.
  • Share reports through your tool of choice, including email and the Fiddler platform.
Fiddler AI prediction discrepency
Fiddler AI pinned insights dashboard
Increase brand loyalty

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.

Zoom in to the details

Loan model decision and explanation types