Dataiku Flipbook

Build Trustworthy AI

Frameworks & Real-World Examples for Trusted AI

To mitigate AI harm and risk, AI practitioners and developers are creating new processes to make AI trustworthy. 

This flipbook highlights how organizations can ensure frontline user adoption of data, models, and apps with trustworthy AI via real-world examples.
How to Build Trustworthy AI Systems_Mockup (3)

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Where Do We Start?

To Garner Stakeholder Trust, Be Sure to Understand:

  • The types of bias in AI systems (i.e., statistical, human, systemic)
  • Key attributes of trustworthiness (i.e., accuracy, reliability, explainability)
  • Ways to put trustworthy AI into practice (i.e., with Dataiku!)

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