AI explainability unlocks the AI black box so we can understand what’s going on inside AI models to ensure AI-driven decisions are transparent, accountable, and trustworthy.
Successful AI deployments come with an enterprise grade explainability solution for teams to inspect, debug and validate models for regulatory or trust purposes. Here are some areas to consider when deciding whether to build or buy such an AI system:
Popular open source explainability packages help teams get basic notebook level visibility but lack enhanced explainability techniques, latest research, wide model coverage, enterprise scale and intuitive interfaces for everyone.
Explainable AI is an active research area where advances are vetted by an engaged research community. An explainability solution should be based on trusted peer reviewed research to avoid explaining a black-box with another black-box.
ML teams can have a breadth of use cases that need different types of models across different frameworks, for example an XGBoost model for sales prediction or a TensorFlow Deep Learning model for fraud detection.
An enterprise-grade production-quality explainability system needs to be fast and reliable, meet compliance needs and scale with the demands of the business.
Systems built in-house have a fixed set of requirements, a high initial cost of development with specialized talent and constant maintenance needs especially with advancing research. Costs for a homegrown enterprise grade explainability solution can exceed $750k over 3 years.
Explaining ML models for validation and risk assessment is a coordinated effort across Data Scientists, Business Owners, Risk and other teams. These users need intuitive experiences for ease of workflow adoption and hand-off.
An effective ML explainability service enables the model development team to get detailed explanations of varying granularities using state of the art explainability techniques on a range of model formats and share them with their stakeholders.
Top ML explainability techniques
Seamless workflow integration
Wide model support
Fiddler provides a comprehensive AI Explainability solution powered by cutting edge explainability research and an industry-first model analytics capability, ‘Slice and Explain’ to address a wide range of model validation, inspection and debugging needs.
Latest in Explainability Research
Industry first Model Analytics
Pluggable into any ML Platform
Top ML model framework support
Patented intuitive experiences
Cloud or on-premise deployment
Built for scale, speed and reliability