Responsible and Explainable AI With Dataiku

Enhance trust and eliminate bias by practicing Responsible AI with Dataiku. Discover how our platform enables you to understand pipelines and interpret model outputs, ensuring Explainable AI projects and initiatives.


Model Interpretation and Explainability

Dataiku empowers you with critical capabilities specifically created for Explainable AI. Benefit from interactive reports on feature importance, partial dependence plots, subpopulation analysis, and individual prediction explanations. 

Understand the factors that influence model predictions in order to make informed decisions that align with Explainable AI principles.


What-if Analysis & Outcome Optimization

Dataiku’s powerful what-if analysis goes beyond simulations into optimization. This interactive feature empowers both technical users and business stakeholders to gain a deeper understanding of model behavior, so they can trust predictive models and apply learned insights in practical ways.  


Disparate Impact and Bias Detection

Dataiku offers a myriad of AI explainability tools within the platform, such as analyses that allow you 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 real-time 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

Teams can stop spending countless hours maintaining project documentation since Dataiku automatically generates comprehensive documentation for models and the project flow. Customizable templates include all the metadata and visualizations needed to snapshot the project state, model design, and results. The final documents are easily shared, to support AI transparency and explainability. 

Use Dataiku’s AI Explain to automatically generate descriptions that explain Dataiku Flows or individual Flow Zones. With robust auto-documentation, organizations maintain consistent records of projects for regulatory compliance and alignment with Responsible AI guidelines.


Explainable Pipelines

Dataiku is designed with explainability at the core of the platform. Each Dataiku project has a visual flow that transparently represents the pipeline of data transformations and movement from start to finish.

Plus, every design decision and step is captured and displayed so current or future team members can clearly follow and explain the sequence of project logic, reducing onboarding times and supporting Responsible AI principles.

Go Further

Responsible AI In Practice

This ebook unpacks the risks of Generative AI and introduces the RAFT (Reliable, Accountable, Fair, and Transparent) framework for Responsible AI.

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Explainable AI in Practice With Dataiku

What exactly is explainable AI and how can explainability be achieved with Dataiku? Find out here.

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