We bring modern technology methods to solve healthcare’s most challenging problems: inequity, bias, and unfairness. We believe in AI for good, AI that is fair, and AI for equity.
Responsible AI is a governance framework that guides Healthcare organizations to address challenges around AI from an ethical and legal perspective.
High performance, always available AI systems that produce reliable outputs
Flexible data model to opt in and out of data sharing
Protect AI systems from risk that may cause physical and digital harm
AI models that include internal and external checks for fair and equitable outcomes
Advancing explainable AI with algorithms, attributes and correlations that are transparent
Health systems and marketplace accountability for the output of AI system decisions
Responsible MLOPs tools are a new, human-centered approach built for the challenges of removing bias and unfairness from machine learning models. Our opensource MLOPs Toolkit and MLOPs Developer Studio (coming Autumn 2022) provide a developer-first experience with an end-to-end fairness framework and functionality that can be selectively applied to your workflows for fitting models that reduce the risk of biased outcomes.