As AI agents increasingly engage in high-stakes decision making, it is essential to assess their accountability in ways that are both fair and interpretable. This involves explaining expected or realized outcomes of multi-agent systems and attributing responsibility for those outcomes to the participating agents. Addressing these challenges is key to fostering societal trust and easing the adoption of AI decision makers. This thesis investigates accountability in multi-agent sequential decision making. We develop methods to attribute responsibility for observed outcomes and overall system performance, design efficient approximation algorithms for otherwise intractable attribution problems, and introduce causal tools to explain how agents’ decisions influence outcomes. Together, these contributions establish theoretical foundations and practical tools for accountable decision making, drawing on and integrating insights from causality, multi-agent reinforcement learning and game theory.

Many modern applications—from large-scale databases to network routers and genome repositories—depend on maintaining large dynamic sets of elements. Efficient management of these sets requires data structures that can quickly support insertions and deletions, answer queries such as "Is this element in the set?" or "What is the value associated with this element?", and assign distinct short keys to elements as the set grows.

The field of data structures is concerned with specifying functionality, abstracting computational models, designing efficient representations, and analyzing the running time and memory requirements of algorithms over these representations. Classical data structures developed for representing sets include dictionaries, retrieval data structures, filters, and perfect hashing.

In this talk, I will explore these issues through the lens of perfect hashing, a method for assigning each element a distinct identifier, or hashcode, with no collisions. We will focus on how to simultaneously satisfy several competing design goals:

Small space: using near-optimal memory proportional to the set’s size.

Fast operations: supporting constant-time insertions, deletions, and queries.

Low redundancy: keeping the range of hashcodes close to the set’s size.

Stability: ensuring that each element’s hashcode remains unchanged while it stays in the set.

Extendability: adapting automatically to unknown or growing data sizes.

This talk is based on joint work with Ioana Bercea.

Millions of everyday users are interacting with technologies built with generative AI, such as voice assistants, search engines, and chatbots. While these AI-based systems are being increasingly integrated into modern life, they can also magnify risks, inequities, and dissatisfaction when providers deploy unreliable systems. A primary obstacle to having more reliable systems is the opacity of the underlying large language models— we lack a systematic understanding of how models work, where critical vulnerabilities may arise, why they are happening, and how models must be redesigned to address them. In this talk, I will first describe my work in investigating large language models to illuminate when models acquire knowledge and capabilities. Then, I will describe my work on building methods to enable data transparency for large language models, that allows practitioners to make sense of the information available to models. Finally, I will describe work on understanding why large language models produce incorrect knowledge, and implications for building the next generation of responsible AI systems.