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Found 42 papers, 5 papers with code
How Hard Is It to Control an Election by Breaking Ties?
Indeed, we prove that it can be NP-hard to control an election by breaking ties even with a two-stage voting rule.
Computational Aspects of Multi-Winner Approval Voting
We study computational aspects of three prominent voting rules that use approval ballots to elect multiple winners.
A Study of Proxies for Shapley Allocations of Transport Costs
We propose and evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting.
Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment
Peer reviews, evaluations, and selections are a fundamental aspect of modern science.
Interdependent Scheduling Games
We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently.
Empirical Evaluation of Real World Tournaments
Computational Social Choice (ComSoc) is a rapidly developing field at the intersection of computer science, economics, social choice, and political science.
Ethical Considerations in Artificial Intelligence Courses
The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses.
The Conference Paper Assignment Problem: Using Order Weighted Averages to Assign Indivisible Goods
Motivated by the common academic problem of allocating papers to referees for conference reviewing we propose a novel mechanism for solving the assignment problem when we have a two sided matching problem with preferences from one side (the agents/reviewers) over the other side (the objects/papers) and both sides have capacity constraints.
A Cost-Effective Framework for Preference Elicitation and Aggregation
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset.
Answering Science Exam Questions Using Query Rewriting with Background Knowledge
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
Incorporating Behavioral Constraints in Online AI Systems
To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints.
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain
To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain.
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
To ensure that agents behave in ways aligned with the values of the societies in which they operate, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society.
Multi-Objective Reinforcement Learning
reinforcement-learning
CPMetric: Deep Siamese Networks for Learning Distances Between Structured Preferences
CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations.
An Interface for Annotating Science Questions
Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set.
Answering Science Exam Questions Using Query Reformulation with Background Knowledge
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
Building Ethically Bounded AI
We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight.
Heuristics in Multi-Winner Approval Voting
In multi-winner approval voting (AV), an agent may vote for as many candidates as they wish.
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
Heuristic Strategies in Uncertain Approval Voting Environments
In real world voting scenarios, people often do not have complete information about other voter preferences and it can be computationally complex to identify a strategy that will maximize their expected utility.
Group Fairness in Bandit Arm Selection
We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting.
A Multi-Channel Neural Graphical Event Model with Negative Evidence
Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains.
PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection
In particular, it does not require an explicit partitioning of the agents, as previous algorithms in the literature.
"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit.
Thinking Fast and Slow in AI
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making.
Modeling Voters in Multi-Winner Approval Voting
In multi-winner approval voting (AV), an agent submits a ballot consisting of approvals for as many candidates as they wish, and winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most approvals.
Peer Selection with Noisy Assessments
In the peer selection problem a group of agents must select a subset of themselves as winners for, e. g., peer-reviewed grants or prizes.
Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations
To this end, we propose a novel inverse reinforcement learning (IRL) method for learning implicit hard and soft constraints from demonstrations, enabling agents to quickly adapt to new settings.
Thinking Fast and Slow in AI: the Role of Metacognition
AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life.
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments
Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning.
When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data
We can invent novel rules on the fly.
Learning Behavioral Soft Constraints from Demonstrations
Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency?
Towards Group Learning: Distributed Weighting of Experts
While a full answer depends on the type of signal, correlation of signals, and desired output, a problem common to all of these applications is that of differentiating sources based on their quality and weighting them accordingly.
Who Pays? Personalization, Bossiness and the Cost of Fairness
The concern is that a bossy user may be able to shift the cost of fairness to others, improving their own outcomes and worsening those for others.
Social Mechanism Design: Making Maximally Acceptable Decisions
Agents care not only about the outcomes of collective decisions but also about how decisions are made.
Who Reviews The Reviewers? A Multi-Level Jury Problem
We consider the problem of determining a binary ground truth using advice from a group of independent reviewers (experts) who express their guess about a ground truth correctly with some independent probability (competence).
Pandering in a Flexible Representative Democracy
We introduce a novel formal model of \emph{pandering}, or strategic preference reporting by candidates seeking to be elected, and examine the resilience of two democratic voting systems to pandering within a single round and across multiple rounds.
Dynamic fairness-aware recommendation through multi-agent social choice
Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks.
Value-based Fast and Slow AI Nudging
Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities.
Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF
Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations.
