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Found 20 papers, 4 papers with code
RankSHAP: a Gold Standard Feature Attribution Method for the Ranking Task
Next, we introduce Rank-SHAP, a feature attribution algorithm for the general ranking task, which is an extension to classical Shapley values.
Percentile Criterion Optimization in Offline Reinforcement Learning
The percentile criterion is approximately solved by constructing an \emph{ambiguity set} that contains the true model with high probability and optimizing the policy for the worst model in the set.
Axiomatic Aggregations of Abductive Explanations
We also evaluate them on multiple datasets and show that these explanations are robust to the attacks that fool SHAP and LIME.
Towards an AI Accountability Policy
This white paper is a response to the "AI Accountability Policy Request for Comments" by the National Telecommunications and Information Administration of the United States.
Simple Steps to Success: Axiomatics of Distance-Based Algorithmic Recourse
We propose a novel data-driven framework for algorithmic recourse that offers users interventions to change their predicted outcome.
Weighted Notions of Fairness with Binary Supermodular Chores
We study the problem of allocating indivisible chores among agents with binary supermodular cost functions.
Dividing Good and Better Items Among Agents with Bivalued Submodular Valuations
This is surprising since for the simpler classes of bivalued additive valuations and binary submodular valuations, MNW allocations are known to be envy free up to any good (EFX).
Yankee Swap: a Fast and Simple Fair Allocation Mechanism for Matroid Rank Valuations
We study fair allocation of indivisible goods when agents have matroid rank valuations.
Model Explanations via the Axiomatic Causal Lens
Thus, our work is the first to formally bridge the gap between model explanations, game-theoretic influence, and causal analysis.
The Price is (Probably) Right: Learning Market Equilibria from Samples
Equilibrium computation in markets usually considers settings where player valuation functions are known.
Model Explanations with Differential Privacy
The drawback is that model explanations can leak information about the training data and the explanation data used to generate them, thus undermining data privacy.
High Dimensional Model Explanations: an Axiomatic Approach
We propose a new axiomatization for a generalization of the Banzhaf index; our method can also be thought of as an approximation of a black-box model by a higher-order polynomial.
Finding Fair and Efficient Allocations When Valuations Don't Add Up
In this paper, we present new results on the fair and efficient allocation of indivisible goods to agents whose preferences correspond to {\em matroid rank functions}.
Weighted Envy-Freeness in Indivisible Item Allocation
We introduce and analyze new envy-based fairness concepts for agents with weights that quantify their entitlements in the allocation of indivisible items.
On the Privacy Risks of Model Explanations
We analyze connections between model explanations and the leakage of sensitive information about the model's training set.
A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts
The past few years have seen several works on learning economic solutions from data; these include optimal auction design, function optimization, stable payoffs in cooperative games and more.
Group-Fairness in Influence Maximization
Influence maximization is a widely used model for information dissemination in social networks.
Computer Science and Game Theory Social and Information Networks
The Price of Quota-based Diversity in Assignment Problems
The two parts of the graph are partitioned into subsets called types and blocks; we seek a matching with the largest sum of weights under the constraint that there is a pre-specified cap on the number of vertices matched in every type-block pair.
Axiomatic Characterization of Data-Driven Influence Measures for Classification
We study the following problem: given a labeled dataset and a specific datapoint x, how did the i-th feature influence the classification for x?
Learning Cooperative Games
This paper explores a PAC (probably approximately correct) learning model in cooperative games.
Computer Science and Game Theory
