Credit Karma leader shares AI governance lessons learned

<p>Not every company has the scale and skills of Intuit’s Credit Karma, but the company’s data science head has some advice on where others can begin devising their own AI governance framework.</p>
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<p>Credit Karma can use Intuit’s <a href=”https://www.techtarget.com/searchsoftwarequality/news/366627890/Intuits-Ashok-Srivastava-on-AI-agents-new-frontier”>GenOS AI operating system</a>, with its catalog of AI models, agents and software development tools. With help from GenOS, teams at Credit Karma recently created a multi-agent system to automatically review AI outputs before allowing them to reach production.</p>
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<img src=”https://cdn.ttgtmedia.com/rms/onlineimages/daianu_madelaine.jpg” alt=”Madelaine Daianu, senior director of data science and engineering, Credit Karma”>Madelaine Daianu
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<p>These form the technical basis for the AI compliance initiative led by Madelaine Daianu, senior director of data science and engineering at Credit Karma. But these efforts began with hands-on human collaboration that other companies can and must emulate, as every company and industry must <a href=”https://www.techtarget.com/healthtechanalytics/feature/How-health-systems-are-facilitating-AI-governance”>devise its own tailored approach</a>.</p>
<p>”Finding a balancing act between innovation and safety, compliance or whatever is relevant to them is extremely important, and taking the step to slow down a little bit before they run and move fast,” Daianu said. “Have your internal red team go and break an LLM-generated response and learn from it, and develop a thorough, custom evaluation framework for your use case.”</p>
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Have your internal red team go and break an LLM-generated response and learn from it, and develop a thorough, custom evaluation framework for your use case.
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<strong>Madelaine Daianu</strong>Senior director of data science and engineering, Credit Karma
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