AI-driven systems increasingly shape consequential decisions in domains such as lending, university admissions, and content moderation. Yet making these systems trustworthy in practice requires more than principled algorithms: it requires methods that scale, account for bias throughout the decision-making process, and can be evaluated against deployed real-world systems. This thesis addresses these challenges through three lines of work: scalable causal algorithmic recourse, fairness across the decision-making pipeline, and content policy enforcement on digital platforms.

First, it introduces CARMA, a neural-network-based approach that amortizes causal recourse generation, producing near-real-time recommendations while preserving causal validity and effort optimality. Second, it addresses fairness across the decision-making pipeline by developing a causal framework for measuring and mitigating bias in post-selection treatment decisions, alongside an online learning framework, FairAll, that learns fair and temporally consistent selection policies without sacrificing utility. Third, it studies instruction-driven moderation with foundation models and introduces ModerationBench, a benchmark of multimodal, in-the-wild social media content grounded in Bluesky’s deployed moderation guidelines.

Together, these contributions push Responsible AI beyond idealized settings and toward practical deployment. They provide scalable mechanisms for recourse, broader tools for fairness across the full decision-making pipeline, and grounded methods for evaluating adaptable content-safety enforcement in real-world digital platforms.

A mix network (mixnet) is a routing network that conceals communication patterns by shuffling, or mixing, the routes of concurrently transmitted messages, thereby providing anonymity for senders, receivers, and sender-receiver pairs. Notable examples of deployed mixnets are Tor and Nym. Given the potential use of mixnets in high-stakes applications, such as protecting whistleblowers, it is essential to establish formal guarantees of sender anonymity, even against powerful adversaries that have a full view of the network and are capable of compromising subsets of mix servers. However, existing analyses of mixnets anonymity typically rely on additional mechanisms, such as noise or chaff messages, or are based on empirical metrics such as entropy, which cannot provide strong guarantees in the presence of adversaries with auxiliary information. My thesis consists of two complementary parts: (1) a first part on parallel mixnets, in which mix nodes operate in loosely synchronized rounds, and (2) a second part on continuous-time mixnets, in which mix nodes operate independently and forward messages after user-specified random delays. First, I present a new analysis of horizontally scalable parallel mixnets, showing that they can achieve strong indistinguishability guarantees for messages without requiring additional noise messages or extensive cryptographic techniques. Second, I develop a theoretical framework for continuous-time mixing by identifying two interacting stochastic processes that govern mixnets' operation: a local shuffling process at each mix node, driven by message delays and their sampling, and a global shuffling process that determines how messages (or batches) propagate between mixing layers. Building on this perspective, I derive a new tractable analytical model that captures mixing at both the local (per-node) and global (system-wide) levels. Finally, I use this model to establish provable anonymity guarantees for asynchronous mixnets.