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Found 15 papers, 7 papers with code
Equitable Pricing in Auctions
We study how pricing affects the division of surplus among buyers in auctions for multiple units.
Trading-off price for data quality to achieve fair online allocation
We consider the problem of online allocation subject to a long-term fairness penalty.
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation
The Shapley value has recently been proposed as a principled tool to achieve this goal due to formal axiomatic justification.
Bounding and Approximating Intersectional Fairness through Marginal Fairness
Then, we prove bounds (easily computable through marginal fairness and other meaningful statistical quantities) in high-probability on intersectional fairness in the general case.
Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model
We lay out the structure of a new geometrical object (the DBN-expohedron), and propose for it a Carath\'eodory decomposition algorithm of complexity $O(n^3)$, where $n$ is the number of documents to rank.
Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankings
Such a decomposition makes it possible to express any feasible target exposure as a distribution over at most $n$ rankings.
On Fair Selection in the Presence of Implicit and Differential Variance
In the second setting (with known variances), imposing the $\gamma$-rule decreases the utility but we prove a bound on the utility loss due to the fairness mechanism.
Scalable Optimal Classifiers for Adversarial Settings under Uncertainty
We consider the problem of finding optimal classifiers in an adversarial setting where the class-1 data is generated by an attacker whose objective is not known to the defender -- an aspect that is key to realistic applications but has so far been overlooked in the literature.
On Fair Selection in the Presence of Implicit Variance
We then compare the utility obtained by imposing a fairness mechanism that we term $\gamma$-rule (it includes demographic parity and the four-fifths rule as special cases), to that of a group-oblivious selection algorithm that picks the candidates with the highest estimated quality independently of their group.
Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek
Resource allocation games such as the famous Colonel Blotto (CB) and Hide-and-Seek (HS) games are often used to model a large variety of practical problems, but only in their one-shot versions.
Computer Science and Game Theory
Nonzero-sum Adversarial Hypothesis Testing Games
We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks.
The Price of Local Fairness in Multistage Selection
In particular, our experiments show that the price of local fairness is generally smaller when the sensitive attribute is observed at the first stage; but globally fair selections are more locally fair when the sensitive attribute is observed at the second stage---hence in both cases it is often possible to have a selection that has a small price of local fairness and is close to locally fair.
Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek
Resource allocation games such as the famous Colonel Blotto (CB) and Hide-and-Seek (HS) games are often used to model a large variety of practical problems, but only in their one-shot versions.
An Approximate Dynamic Programming Approach to Adversarial Online Learning
We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses.
Linear Regression from Strategic Data Sources
In this paper, we study a setting in which features are public but individuals choose the precision of the outputs they reveal to an analyst.
