Explain Complex Model Predictions and Issues at Scale with Censius
Dissect Black Box AI
Explain the ‘why’ behind complex predictions to stakeholders
Reduce Time-to-Resolve Issues
Detect and eliminate model issues in record time
Deliver Consistent Performance
Continuously maintain performance scores of hundreds of models
Eliminate Model Risks
Prevent negative impact on
end-user with proactive diagnosis
Reduce Model Risks and Time-to-Resolve Issues
Explainable AI locates root causes behind model issues in record time and saves end-user from the harsh impact of poor predictions.
Detect causes behind performance regression
Dissect and resolve issues in real time
Proactively detect suspicious patterns
Establish Trust with High Model Transparency
Explain black box AI decisions to both internal stakeholders and customers.
Convey prediction logic to business teams
Explain issues to customers with high clarity
Manage and resolve escalations with ease
Tone Down the Cost of AI Mistakes
Explainable AI offers a thorough and quick diagnosis for a variety of model issues and is the perfect enabler for damage control.
Enable smooth user experience even after issue detection
Explain and manage hundreds of issues consecutively
Increase volume of ticket resolution
Empower your Machine Learning Team with Censius Explainability
Explain model decisions and perform root cause analysis across the global data.
Explain model decisions and perform root cause analysis for particular data segments which are either sensitive or critical.
Explain model decisions and perform root cause analysis for individual predictions or data points.
Detect Causal Relationships
Observe causal patterns between various variables and metrics with customizable visuals.
Locate Impactful Features
Use widgets imbibed with research-proven techniques to locate suspicious features.
Sensitize Against Outliers
Leverage distributions and meta features to immediately identify anomalous behaviour.
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Plug in a toolbox of ready-to-use explainability widgets in just a few clicks
Feature Impact Assessment
Leverage multiple research-proven techniques combined to assess feature importance
Visualize and observe changing trends or anomalous patterns in feature distributions.
Find causal patterns between various performance metrics and drift indicators.
Assess metadata for the most impactful features and associate them with the available distributions.
Segment data based on domain knowledge or data assessment to locate sensitive cohorts.
Track the ups and downs of performance metrics to identify dipping patterns.