Panelist Radhika Alla

Radhika Alla

Mayo Clinic

United States

Radhika Alla is vice chair of digital platforms at Mayo Clinic, where she leads its efforts to deliver a seamless digital customer experience for patients, care teams, and other stakeholders. Previously, she held various leadership roles across Cigna’s U.S. and international businesses, including serving as chief digital officer for the company’s government business. She has also held technology leadership roles at BroadVision and Northwestern Mutual and consulted for Fortune 50 companies. Alla mentors women at various career stages and is a member of the Forbes Technology Council.

Voting History

Statement	Response
Most RAI programs are unprepared to address the risks of new generative AI tools. Strongly agree	“Most RAI programs address the risks of traditional AI systems, which focus on detecting patterns, making decisions, honing analytics, classifying data, and detecting fraud. On the other hand, generative AI uses machine learning to process a vast amount of visual or textual data, much of which is scraped from unknown sources on the internet, and then determines what things are most likely to appear near other things. Due to the nature of data sets used in generative AI tools and potential biases in those data sets from unknown data sources, most RAI programs are unprepared to address the risks of new generative AI tools.”
RAI programs effectively address the risks of third-party AI tools. Agree	“RAI is a journey to trust. RAI programs and frameworks need continuous oversight and governance to ensure that the key principles of RAI are applicable and relevant to evaluating third-party AI tools and are augmented where needed. Organizations that are evaluating third-party AI tools should consider what levels of bias exist in the data that is used to build the algorithms and models. Measurement of bias is a critical step in testing outcomes and evaluating the effects. The same RAI rigor used to develop in-house AI models should be applied to evaluating third-party AI platforms and services.”

Statement

Response

Most RAI programs are unprepared to address the risks of new generative AI tools. Strongly agree

“Most RAI programs address the risks of traditional AI systems, which focus on detecting patterns, making decisions, honing analytics, classifying data, and detecting fraud. On the other hand, generative AI uses machine learning to process a vast amount of visual or textual data, much of which is scraped from unknown sources on the internet, and then determines what things are most likely to appear near other things.

Due to the nature of data sets used in generative AI tools and potential biases in those data sets from unknown data sources, most RAI programs are unprepared to address the risks of new generative AI tools.”

RAI programs effectively address the risks of third-party AI tools. Agree

“RAI is a journey to trust. RAI programs and frameworks need continuous oversight and governance to ensure that the key principles of RAI are applicable and relevant to evaluating third-party AI tools and are augmented where needed. Organizations that are evaluating third-party AI tools should consider what levels of bias exist in the data that is used to build the algorithms and models. Measurement of bias is a critical step in testing outcomes and evaluating the effects. The same RAI rigor used to develop in-house AI models should be applied to evaluating third-party AI platforms and services.”