Generative models, such as ChatGPT and DALL-E, are used by millions of people daily for tasks ranging from programming and content creation to resume filtering. These models often create the impression of being “intelligent,” which can incentivize careless use in critical applications. While generative models are empowering, they appear to be black boxes, and their misuse can result in harmful or unlawful outcomes.
In this talk, I will present algorithms and tools for dissecting and analyzing generative models using holistic, causal, and data-centric approaches.
By applying these methods to state-of-the-art models, we can foster trust in these technologies by uncovering human-interpretable concepts that underpin their behavior, scrutinizing their extensive training data, and evaluating their learning processes.
Finally, I will reflect on how generative models have transformed the field of AI and discuss the challenges that remain in ensuring their responsible development and use.
