Towards Responsible AI

Brinnae Bent, Duke UniversityFollow

Document Type

Presentation

Abstract

As Artificial Intelligence is adopted in a variety of domains, from healthcare to finances to entertainment, ensuring its responsible development is critical, using interpretable models, transparent techniques, and robust evaluation methods. However, significant challenges remain in implementing these principles. In this talk, Dr. Bent will explore the current gaps in Responsible AI development, highlighting areas where improvement is needed. The discussion will cover emerging research trends addressing these challenges, including advancements in explainable AI, interpretable ML, and AI safety. Dr. Bent will also share best practices for practitioners aiming to develop and deploy AI responsibly. This talk will provide insights for technologists, researchers, educators, policymakers, and business leaders navigating the complex intersection of AI capabilities and ethical considerations.

Author Bio

Brinnae Bent, PhD, is a faculty member in Artificial Intelligence at Duke University, an AI research scientist, and a startup advisor. She is a leader in bridging the gap between research and industry in machine learning, having led projects and developed algorithms for the largest companies in the world. More importantly, she has built algorithms that have meaningful impacts - from helping people walk to noninvasively monitoring glucose. Brinnae is an accomplished researcher and ML practitioner, with over 30 publications and a breadth of experience developing algorithms across domains including health/wellness, sports, privacy, interior design, and energy. Her research on digital biomarkers is world-renowned and her emerging work on Responsible AI strives to solve important problems in the application of AI to real-world problems.

Keywords

Responsible AI, Explainable AI, Interpretable ML, AI Safety, Ethical AI

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Towards Responsible AI

As Artificial Intelligence is adopted in a variety of domains, from healthcare to finances to entertainment, ensuring its responsible development is critical, using interpretable models, transparent techniques, and robust evaluation methods. However, significant challenges remain in implementing these principles. In this talk, Dr. Bent will explore the current gaps in Responsible AI development, highlighting areas where improvement is needed. The discussion will cover emerging research trends addressing these challenges, including advancements in explainable AI, interpretable ML, and AI safety. Dr. Bent will also share best practices for practitioners aiming to develop and deploy AI responsibly. This talk will provide insights for technologists, researchers, educators, policymakers, and business leaders navigating the complex intersection of AI capabilities and ethical considerations.