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.

Artificial intelligence (AI) systems are increasingly being used to support human experts in various domains such as healthcare, education, and the judicial system. The aim of these systems is complementarity—leveraging the strengths of each side, human and AI, to compensate for the weaknesses of the other. In most such systems, the human expert makes decisions based on a prediction by the AI model and their own judgment. However, models designed for automated decision making are typically trained in isolation and do not take into account the human decision maker when making predictions. As a result, when these AI models are used in decision support systems, their predictions may not be helpful, undermining the human expert’s trust in the AI model and leading to no improvement in their decisions. To address this, this thesis focuses on the design of AI-based decision support systems that leverage the interaction with the expert through counterfactual reasoning and uncertainty quantification. It proposes decision support systems for three distinct decision-making contexts, where each one is based on a novel methodological approach and is evaluated with experiments using real-world data or a human subject study.

Machine learning has transformed many areas of science and technology, including the life sciences, most prominently through its breakthrough impact on protein structure prediction, recognized by the 2024 Nobel Prize in Chemistry. An open question, however, is whether machine learning can have a similarly profound impact on biomarker discovery, that is, the identification of biological properties that predict system functions or phenotypes. Biomarker discovery is a key topic for advancing biology and medicine. In this talk, I will present our efforts to harness machine learning for biomarker discovery, summarize our algorithmic contributions, and discuss the opportunities and challenges in this field.

Building trustworthy software systems has become increasingly challenging as complexity grows across the hardware-software stack. Adversaries exploit sophisticated techniques such as return-oriented programming and timing side channels to bypass traditional defenses and compromise critical components. This talk examines these classes of low-level attacks and presents defenses we have developed, including control-flow integrity mechanisms and memory tagging. I will further discuss how automated approaches such as fuzzing can help us to systematically expose latent vulnerabilities and strengthen the design of security-critical systems, aiming for resilience against both current and emerging threats. I will conclude with an overview of future challenges.