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Explainability with Dataiku

Practice Responsible AI by understanding pipelines and interpreting model outputs to increase trust and eliminate bias.

 

Model Interpretation and Explainability

Dataiku provides critical capabilities for explainable AI, including interactive reports for feature importance, partial dependence plots, subpopulation analysis, and individual prediction explanations. 

Together, these techniques help explain how a model makes decisions and enable data scientists and stakeholders to understand the factors influencing model predictions. 

 

What-if Analysis & Outcome Optimization

Powerful what-if analysis allows both technical users and business experts to test different combinations of inputs and review the impact to predicted results. Simulation capabilities even enable teams to systematically uncover and prescribe changes that would lead to the desired business outcomes. 

With this visual interface for what-if analysis, business users can build trust in predictive models and apply knowledge of model behavior in practical ways. 

 

Disparate Impact and Bias Detection

Dataiku provides analyses to uncover segments that may be unfairly or differently treated by a model. Interactive subpopulation analysis allows users to compare model results by group, and disparate impact analysis measures whether a sensitive group receives a given outcome at a rate close to that of the advantaged group. 

With this information in hand, data scientists can produce models that deliver more responsible and equitable outcomes.

 

Individual Prediction Explanations

Dataiku generates row-level prediction explanations (ICE and SHAP) to provide additional information for predicted results.

For both batch and realtime scoring, prediction explanations can be returned as part of the response, which fulfills the need to have reason codes in regulated industries and provides additional information for analysis. 

 

Automatic Flow and Model Documentation

Dataiku automatically generates comprehensive documentation for models and the project flow on a one-off or scheduled basis. Customizable templates include all the metadata and visualizations needed to snapshot the project state or model design and results. 

With robust auto-documentation, organizations maintain consistent records of AI projects for regulatory compliance, and teams can stop spending countless hours maintaining project docs. 

 

Explainable Pipelines

Each Dataiku project has a visual flow that transparently represents the pipeline of data transformations and movement from start to finish. Every design decision and step are captured and displayed so current or future team members can clearly follow and explain the sequence of project logic.

Go Further

Responsible AI In Practice

This blog series covers what happens when AI systems make a mistake, and why that matters.

Read the Series

Get a Demo

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Explainable AI: From Theory to Practice

Discover the tradeoffs between white-box and black-box models, Dataiku's approach to explainable AI features, and more.

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Explainable AI: What and How to Execute

In this ebook, learn how to reduce business risk and infuse transparency into AI initiatives.

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