Addressing bias, fairness, and the societal impact of machine learning.
Understanding and mitigating fairness issues in AI systems.
Techniques for making 'black box' models more interpretable and transparent.
Exploring what's next: large language models, multimodal AI, and more.
Considering the broader effects of ML on society, jobs, and privacy.