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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data. These risks undermine the underlying trust in AI and affect your organization’s ability to deliver successful AI projects, unhindered by potential ethical and reputational consequences.

Are you ready to deliver fair, unbiased, and trustworthy AI?

Download this guide to find out:

  • How to build an end-to-end process of identifying, investigating, and mitigating bias in AI
  • How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models
  • How to successfully navigate the bias versus accuracy trade-off for final model selection and much more
Trust is not an option, it is a requirement. Building AI systems without trust tenets invites disaster, for your organization, your own personal brand and for stakeholders impacted by the AI system.
Ted Kwartler
Ted Kwartler

Field CTO, DataRobot

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