Brandon Fain

Conference ACM Facct 2026 Proceedings of the 9th Annual ACM Conference on Fairness Accountability and Transparency · June 25, 2026 The constitutional framework of alignment aims to align large language models (LLMs) with value-laden principles written in natural language (such as to avoid using biased language). Prior work has focused on parameter fine-tuning techniques, such as reinf ... Full text Cite

Learning Alignment Principles Grounded in Human Reasons and Values

Conference ACM Facct 2026 Proceedings of the 9th Annual ACM Conference on Fairness Accountability and Transparency · June 25, 2026 A crucial consideration when developing and deploying Large Language Models (LLMs) is the human values to which these models are aligned. In the constitutional framework of alignment models are aligned to a set of principles (the constitution) specified in ... Full text Cite

Multi-objective Reinforcement Learning with Nonlinear Preferences: Provable Approximation for Maximizing Expected Scalarized Return

Conference Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas · January 1, 2025 We study multi-objective reinforcement learning with nonlinear preferences over trajectories. That is, we maximize the expected value of a nonlinear function over accumulated rewards (expected scalarized return or ESR) in a multi-objective Markov Decision ... Cite

Fairness in the Assignment Problem with Uncertain Priorities

Conference Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas · January 1, 2023 In the assignment problem, a set of items must be allocated to unit-demand agents who express ordinal preferences (rankings) over the items. In the assignment problem with priorities, agents with higher priority are entitled to their preferred goods with r ... Cite

Welfare and Fairness in Multi-objective Reinforcement Learning

Conference Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas · January 1, 2023 We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward. Motivated by the fair resource allocation literature, we model this as an ... Cite

Auditing for Gerrymandering by Identifying Disenfranchised Individuals

Conference ACM International Conference Proceeding Series · June 21, 2022 Gerrymandering is the practice of drawing congressional districts to advantage or disadvantage particular electoral outcomes or population groups. We study the problem of computationally auditing a districting for evidence of gerrymandering. Our approach i ... Full text Cite

Budget sharing for multi-analyst differential privacy

Conference Proceedings of the VLDB Endowment · January 1, 2021 Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and policy making problems. These organizations are also legally required to prote ... Full text Cite

Random dictators with a random referee: Constant sample complexity mechanisms for social choice

Conference 33rd Aaai Conference on Artificial Intelligence Aaai 2019 31st Innovative Applications of Artificial Intelligence Conference Iaai 2019 and the 9th Aaai Symposium on Educational Advances in Artificial Intelligence Eaai 2019 · January 1, 2019 We study social choice mechanisms in an implicit utilitarian framework with a metric constraint, where the goal is to minimize Distortion, the worst case social cost of an ordinal mechanism relative to underlying cardinal utilities. We consider two additio ... Full text Cite

Proportionally Fair Clustering

Conference Proceedings of Machine Learning Research · January 1, 2019 We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context. For clustering n points with k centers, we define fairness as proportionality to mean that any n/k points are entitled to for ... Cite

Fair allocation of indivisible public goods

Conference ACM EC 2018 Proceedings of the 2018 ACM Conference on Economics and Computation · June 11, 2018 We consider the problem of fairly allocating indivisible public goods. We model the public goods as elements with feasibility constraints on what subsets of elements can be chosen, and assume that agents have additive utilities across elements. Our model g ... Full text Cite

ROBUS: Fair cache allocation for data-parallel workloads

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · May 9, 2017 Systems for processing big data-e.g., Hadoop, Spark, and massively parallel databases-need to run workloads on behalf of multiple tenants simultaneously. The abundant disk-based storage in these systems is usually complemented by a smaller, but much faster ... Full text Cite

Sequential deliberation for social choice

Conference Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics · January 1, 2017 Social choice is a normative study of designing protocols for collective decision making. However, in instances where the underlying decision space is too large or complex for ordinal voting, standard voting methods may be impractical. How then can we desi ... Full text Cite

The core of the participatory budgeting problem

Conference Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics · January 1, 2016 In participatory budgeting, communities collectively decide on the allocation of public tax dollars for local public projects. In this work, we consider the question of fairly aggregating preferences to determine an allocation of funds to projects. We argu ... Full text Cite