1,721,075 research outputs found
Externalities in Cake Cutting
Get PDFThe cake cutting problem models the fair division of a heterogeneous good between multiple agents. Previous work assumes that each agent derives value only from its own piece. However, agents may also care about the pieces assigned to other agents; such externalities naturally arise in fair division settings. We extend the classical model to capture externalities, and generalize the classical fairness notions of proportionality and envy-freeness. Our technical results characterize the relationship between these generalized properties, establish the existence or nonexistence of fair allocations, and explore the computational feasibility of fairness in the face of externalities
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Towards Principled AI Alignment: An Evaluation and Augmentation of Inverse Constitutional AI
No full textThe accelerated pace of development for advanced AI systems motivates an examination of whether such systems are actually aligned to human designer and user intent. The notion of human intent, however, remains highly ambiguous, with ongoing debate regarding whether large language models (LLMs) should be aligned to demonstrated behavior communicated through the expression of human preferences or abstract normative principles defined by collective deliberation. Methods such as reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) demonstrate approaches to AI alignment that depend on the communication of human preferences. The formalization of Constitutional AI (CAI), which leverages reinforcement learning from AI feedback (RLAIF) and involves the communication of a set of normative principles for directing the behavior of an LLM, motivates the development of scalable approaches to bridge the gap between standard alignment methods that employ human preference data and interpretable, principled AI alignment. Inverse Constitutional AI (ICAI) is a new framework that aims to learn a constitution from human preference data that may serve as both an interpretable compression of human preference and an instructive set of principles for aligning LLM behavior.
In this thesis, we present an expanded implementation of the ICAI framework that addresses the desideratum of representation for varied human preferences by electing a principle committee using algorithms drawn from the theory of approval voting in social choice. We describe relevant metrics for evaluating the efficacy of a constitution constructed through the implementation of the framework and provide a method of systematic evaluation for all components of the pipeline. We hope that the improved formalization of this approach to principled model alignment will contribute to the development and deployment of more interpretable and better aligned AI systems.Computer Scienc
DEMOCRATIX: A Declarative Approach to Winner Determination
No full textComputing the winners of an election is an important subtask in voting and preference aggregation. The declarative nature of answer-set programming (ASP) and the performance of state-of-the-art solvers render ASP very well-suited to tackle this problem. In this work we present a novel, reduction-based approach for a variety of voting rules, ranging from tractable cases to problems harder than NP. In addition, we discuss how encodings of voting rules can be optimized and combined in our approach. The encoded voting rules are put together in the extensible tool DEMOCRATIX, which handles the computation of the winners and is also available as a web application. To learn more about the capabilities and limits of the approach, the encodings are evaluated thoroughly on real-world data as well as on random instances
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Algorithms for Managing Deliberation
No full textCitizens’ assemblies, in which ordinary people are randomly selected to participate in the policymaking process, have become increasingly widespread in recent decades. Chosen via a method known as sortition, these assemblies have not only had a profound impact on worldwide legislation but also sparked a flurry of recent research into how they can best be organized, from recruiting and selecting participants to managing the deliberation itself. In this report, we focus on the assembly partitioning problem, in which we wish to partition participants among a set of moderated groups over multiple sessions of deliberation such that 1) each group is representative of the assembly, and 2) participants get to interact with as many new people as possible over the course of deliberation. We first provide an overview of the baseline algorithm that
is being used to generate partitions. We then define a model for the problem and propose a greedy, linear programming-based approach to tackling it. Afterwards, we conduct a series of experiments to compare our algorithm with the baseline, demonstrating that our algorithm generates significantly higher-quality solutions on both synthetic and real-world assembly data. We also derive a theoretical bound on the maximum number of unique interactions that can be achieved between back-to-back partitions. Finally, we discuss the advantages of our algorithm over the previous approach, advocating that organizers adopt our algorithm for future assemblies
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Multi-Persona Oracles for Fair Classification
No full textAs machine learning systems are increasingly deployed in high-stakes domains, incorporating fairness constraints into model training has become a central challenge. Most fairness-aware algorithms assume access to an
idealized human fairness oracle—a source of supervision that is difficult to obtain at scale. Motivated by theories
of value pluralism and drawing on ideas from generative social choice, we introduce the Multi-Persona Oracle
Framework, which uses large language model (LLM) personas to simulate diverse, subjective perspectives on fairness, aiming to more effectively bridge theory with practice.
We collect pairwise fairness judgments from 815 synthetic judges, each representing a unique combination
of personality traits, racial identity, and ideological background. These judgments are elicited using carefully
designed prompts and applied to 200 training and 800 test comparisons drawn from the COMPAS Recidivism dataset. We
extend a no-regret learning framework for fairness-constrained classification, using these constraint sets to train
classifiers and evaluate their generalization across unseen individuals and judges.
We analyze generalization patterns at the level of individual judges, demographically grouped personas, and
two baselines: a default LLM and an expert fairness-oriented persona. To assess robustness, we sweep over a range
of fairness slack parameters γ and report accuracy alongside average and maximum fairness violations on heldout test constraints. In our proof-of-concept case study, our findings show that training on ensembles of judges
yields strong generalization to fairness constraints in out-of-sample holdout sets, due to the complexity of fairness
judgments and the nature of the Logistic Regression model.Applied Mathematic
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Abating gerrymandering by mandating fairness
Get PDFPolitical redistricting has been at the center of a rancorous public and legal debate overvoting rights and partisanship in the U.S. Even in cases where there is a desire to craft districtings that are acceptable to both sides of the aisle, it is unclear how to do so. Our proposed approach to this problem combines fair division and optimization; at its heart is a rigorous notion of fairness for districtings, which we call the fair coin flip guarantee. We apply our approach to district four U.S. states, and find that enforcing fairness does not come at a significant cost to traditional measures of quality.First author draf
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