<ul data-eligibleForWebStory="true"><li>Society's reliance on AI and ML is increasing, but the question of trusting AI outputs remains critical.</li><li>Uncertainty quantification is essential to understand AI model outputs and build trust.</li><li>Human-in-the-loop systems like medical AI require trust but risk misdiagnosis without uncertainty quantification.</li><li>Monte Carlo methods offer robust uncertainty quantification but are slow and compute-intensive.</li><li>New computing platforms are emerging to automate uncertainty quantification and improve processing speed.</li><li>Recent developments have reduced barriers to uncertainty quantification, enabling faster analyses.</li><li>The future of AI/ML trustworthiness hinges on advanced computation and implementing uncertainty quantification.</li><li>Organizations must prioritize trust in AI by implementing uncertainty quantification to engender consumer trust.</li><li>New computing technologies are simplifying the deployment of uncertainty quantification in AI solutions.</li><li>Demand for explainability and uncertainty quantification in AI deployments is increasing.</li></ul>

Prioritizing Trust in AI

Discover more