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In this seminar, Matt Bedsole explored how Lowe’s has approached developing a scalable model monitoring framework to maintain AI reliability in production. He discussed key challenges in AI observability—from detecting data drift to improving model explainability—and shared real-world insights on operationalizing monitoring at scale.

In this seminar, Dr. Miroshnikov explored algorithmic approaches to fair lending that promote more transparent and accountable financial decision-making.

Several members of TAIMing AI, alongside leading industry researchers, gathered at the Joint Mathematics Meetings 2025 in Seattle for a special session on Trustworthy AI Applications.

In this seminar, Dr. Gurcan discussed the transformative impact of AI in healthcare, highlighting its potential to reduce diagnostic disparities, enhance treatment strategies, and address challenges like workforce shortages and health inequities

The Center for TAIMing AI published its inaugural newsletter on November 26, 2024, the newsletter included the following note from the Director

Prof. Jake Lee delivered a seminar that explored the delicate balance between privacy preservation and trustworthiness in example-based learning, highlighting the challenges and opportunities this approach offers for AI systems. He discussed how example-based learning enhances AI explainability and efficiency, while also addressing concerns about data privacy and the need for robust safeguards.

Dr. Chase Joyner, a Senior Data Scientist at Teladoc Health, led a seminar on the need to minimize bias in AI systems used in healthcare. He highlighted methods to address inequities, particularly those impacting marginalized populations, and outlined the need for ethical AI practices to ensure equitable outcomes.

UNC Charlotte has joined more than 200 of the nation’s leading artificial intelligence (AI) stakeholders to participate in a Department of Commerce initiative to support the development and deployment of trustworthy and safe AI.

Dr. Agus Sudjianto led a seminar discussing how performance and accuracy alone are insufficient for machine learning models, especially in high-risk industries, and should be supplemented by other metrics like risk assessment and interpretability.

Dr. Michael Pokojovy presented a groundbreaking AI system designed for quick and accurate COVID-19 diagnosis via chest X-rays. The model uses deep learning models to classify X-rays, aiming to assist healthcare providers with early diagnosis