Individual Fairness in Advertising Auctions through Inverse Proportionality

• URL: http://arxiv.org/abs/2003.13966v3
• Date: Wed, 1 Dec 2021 03:16:03 GMT
• Title: Individual Fairness in Advertising Auctions through Inverse Proportionality
• Authors: Shuchi Chawla, Meena Jagadeesan
• Abstract summary: We study the design of ad auctions that, given fair bids, are guaranteed to produce fair outcomes. We introduce a new class of allocation algorithms that achieve a tradeoff between fairness and social welfare.
• Score: 12.861470300253329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent empirical work demonstrates that online advertisement can exhibit bias in the delivery of ads across users even when all advertisers bid in a non-discriminatory manner. We study the design of ad auctions that, given fair bids, are guaranteed to produce fair outcomes. Following the works of Dwork and Ilvento (2019) and Chawla et al. (2020), our goal is to design a truthful auction that satisfies ``individual fairness'' in its outcomes: informally speaking, users that are similar to each other should obtain similar allocations of ads. Within this framework we quantify the tradeoff between social welfare maximization and fairness. This work makes two conceptual contributions. First, we express the fairness constraint as a kind of stability condition: any two users that are assigned multiplicatively similar values by all the advertisers must receive additively similar allocations for each advertiser. This value stability constraint is expressed as a function that maps the multiplicative distance between value vectors to the maximum allowable $\ell_{\infty}$ distance between the corresponding allocations. Standard auctions do not satisfy this kind of value stability. Second, we introduce a new class of allocation algorithms called Inverse Proportional Allocation that achieve a near optimal tradeoff between fairness and social welfare for a broad and expressive class of value stability conditions. These allocation algorithms are truthful and prior-free, and achieve a constant factor approximation to the optimal (unconstrained) social welfare. In particular, the approximation ratio is independent of the number of advertisers in the system. In this respect, these allocation algorithms greatly surpass the guarantees achieved in previous work. We also extend our results to broader notions of fairness that we call subset fairness.

Related papers

• Fair Allocation in Dynamic Mechanism Design [57.66441610380448]
  We consider a problem where an auctioneer sells an indivisible good to groups of buyers in every round, for a total of $T$ rounds. The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group.
  arXiv  Detail & Related papers  (2024-05-31T19:26:05Z)
• Advancing Ad Auction Realism: Practical Insights & Modeling Implications [2.8413290300628313]
  This paper shows that one can still gain useful insight into modern ad auctions by modeling advertisers as agents governed by an adversarial bandit algorithm. We find that soft floors yield lower revenues than suitably chosen reserve prices, even restricting attention to a single query.
  arXiv  Detail & Related papers  (2023-07-21T17:45:28Z)
• Bidding Strategies for Proportional Representation in Advertisement Campaigns [8.269283912626873]
  We show that equitable bidding may not result in equitable outcomes due to heterogeneous levels of competition for different types of individuals. We consider alterations that make no change to the platform mechanism and instead change the bidding strategies used by advertisers.
  arXiv  Detail & Related papers  (2023-05-22T23:29:05Z)
• Fair Without Leveling Down: A New Intersectional Fairness Definition [1.0958014189747356]
  We propose a new definition called the $alpha$-Intersectional Fairness, which combines the absolute and the relative performance across sensitive groups. We benchmark multiple popular in-processing fair machine learning approaches using our new fairness definition and show that they do not achieve any improvement over a simple baseline.
  arXiv  Detail & Related papers  (2023-05-21T16:15:12Z)
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• Fairness in Matching under Uncertainty [78.39459690570531]
  algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings. We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits. We design a linear programming framework to find fair utility-maximizing distributions over allocations.
  arXiv  Detail & Related papers  (2023-02-08T00:30:32Z)
• Proportional Fairness in Obnoxious Facility Location [70.64736616610202]
  We propose a hierarchy of distance-based proportional fairness concepts for the problem. We consider deterministic and randomized mechanisms, and compute tight bounds on the price of proportional fairness. We prove existence results for two extensions to our model.
  arXiv  Detail & Related papers  (2023-01-11T07:30:35Z)
• Robust Allocations with Diversity Constraints [65.3799850959513]
  We show that the Nash Welfare rule that maximizes product of agent values is uniquely positioned to be robust when diversity constraints are introduced. We also show that the guarantees achieved by Nash Welfare are nearly optimal within a widely studied class of allocation rules.
  arXiv  Detail & Related papers  (2021-09-30T11:09:31Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Adversarial Learning for Counterfactual Fairness [15.302633901803526]
  In recent years, fairness has become an important topic in the machine learning research community. We propose to rely on an adversarial neural learning approach, that enables more powerful inference than with MMD penalties. Experiments show significant improvements in term of counterfactual fairness for both the discrete and the continuous settings.
  arXiv  Detail & Related papers  (2020-08-30T09:06:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.