Latent Space Exploration for Safe and Trustworthy AI Models

The recent performance advancement in deep neural network models has substantially increased their dissemination to vast application areas. Given this widespread adoption, ensuring the safety and trustworthiness of AI models is more critical than ever before. A standard way for assessing AI model performance involves extrinsic evaluation across a set of downstream tasks. While effective for advancing state-of-the-art, these evaluations offer limited insights into how models learn and solve a task. In this talk, I advocate for a deeper exploration of model internals, particularly their latent space, to fully test their capabilities, to build better models and to increase trust in them. I will present a few use cases to support this stance. For instance, the intrinsic dimensionality trend of models explains the robustness-generalization tradeoff during adversarial training, informing the design of robust and scalable adversarial methods without compromising generalization. Moreover, the study of the structure and representation of knowledge within latent space is effective in evaluating the language comprehension capabilities of models and enables interpretation of their predictions.

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