“Explainable AI and interpretable ML enable us to use knowledge, rather than numbers, to better understand the predictions made by a model and subsequently the decisions humans make based on these predictions,” Bent said.
Explainable AI and interpretable ML enable us to use knowledge, rather than numbers, to better understand the predictions made by a model and subsequently the decisions humans make based on these predictions.
Brinnae BentExecutive in Residence, Duke MEng in AI
The program includes three courses: Developing Explainable AI, Interpretable Machine Learning and Explainable Machine Learning.
These courses are designed to empower future AI leaders with the skills needed to design solutions for real-world challenges that are both powerful and ethically responsible.
As AI has rapidly taken the world by storm, the website notes, entering and transforming fields like healthcare and finance, and shaping our everyday lives through social media, the ability to develop AI systems that are accurate, transparent and trustworthy is critical.
“I strongly believe anyone who is using or building AI systems should learn more about developing responsible AI,” Bent said. “As these systems are increasingly being used for critical decision making, it becomes imperative that those building these systems are considering AI understandability.”
Brinnae Bent (front), during her time as a PhD student at Duke, preparing to download information from a wearable health monitoring device with Professor Jessilyn Dunn (back).
By exploring topics like SOTA interpretable machine learning approaches, mechanistic interpretability, and explainable AI in Large Language Models (LLMs), learners will be able to achieve Bent’s goals. Students will leave the course with the tools to “enhance the transparency and accountability of AI systems, build trust with users and stakeholders, and address ethical and privacy concerns in AI development,” according to the course website.
The 35-hour program created by Duke’s AI Master of Engineering program and Duke’s Learning Innovation and Lifetime Education (LILE) is open to anyone around the world, aligning with Duke’s institutional goals of being a leader in the AI field by making this knowledge accessible to everyone.
As AI is continuously changing our lives, Bent finds joy in being a part of this technological journey.
“I love teaching these topics because the topics are emerging,” Bent said. “This is a very fast-paced field. Much of the content of the courses is only a few years old.”
She said that course content on new fields like mechanistic interpretability and explainable AI in generative computer vision includes research from periods as early as summer 2024.
With transparency, interpretability and explainability at the core, this course will be Coursera’s first offering on XAI and will be the first certified online course to cover mechanistic interpretability and XAI in LLMs, Bent said.
Duke students and alumni will receive free access to the course. All students who complete the course will earn a Duke career certificate.
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