ABSTRACT:

As predictive and generative models are increasingly being deployed in various high-stakes applications in critical domains including healthcare, law, policy and finance, it becomes important to ensure that relevant stakeholders understand the behaviors and outputs of these models so that they can determine if and when to intervene. To this end, several techniques have been proposed in recent literature to explain these models. In addition, multiple regulatory frameworks (e.g., GDPR, CCPA) introduced in recent years also emphasized the importance of enforcing the key principle of “Right to Explanation” to ensure that individuals who are adversely impacted by algorithmic outcomes are provided with an actionable explanation. In this talk, I will discuss the gaps that exist between regulations and state-of-the-art technical solutions when it comes to explainability of predictive and generative models. I will then present some of our latest research that attempts to address some of these gaps. I will conclude the talk by discussing bigger challenges that arise as we think about enforcing right to explanation in the context of large language models and other large generative models.

ZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/94929617939