an explainable AI venture

 we leverage a proprietary algorithm to create explainable AI

models that are compliant, accurate, comprehensible and trusted

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The accuracy of AI models has grown dramatically over the past few years

However, the most accurate models are "black boxes" and do not explain why they have made certain decisions or recommendations.

This created a huge challenge

especially with the surge of new regulations requiring the need for AI explainability in specific verticals, including finance, insurance and healthcare.

In order to leverage the advancements in AI technology

organizations must adopt explainability, which also benefits data science teams, business stakeholders and end users.

"Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead"


Professor Cynthia Rudin

Director of the Prediction Analysis Lab at Duke University


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