Social Diversity Reduces the Complexity and Cost of Fostering Fairness
- URL: http://arxiv.org/abs/2211.10517v1
- Date: Fri, 18 Nov 2022 21:58:35 GMT
- Title: Social Diversity Reduces the Complexity and Cost of Fostering Fairness
- Authors: Theodor Cimpeanu, Alessandro Di Stefano, Cedric Perret and The Anh Han
- Abstract summary: We investigate the effects of interference mechanisms which assume incomplete information and flexible standards of fairness.
We quantify the role of diversity and show how it reduces the need for information gathering.
Our results indicate that diversity changes and opens up novel mechanisms available to institutions wishing to promote fairness.
- Score: 63.70639083665108
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Institutions and investors are constantly faced with the challenge of
appropriately distributing endowments. No budget is limitless and optimising
overall spending without sacrificing positive outcomes has been approached and
resolved using several heuristics. To date, prior works have failed to consider
how to encourage fairness in a population where social diversity is ubiquitous,
and in which investors can only partially observe the population. Herein, by
incorporating social diversity in the Ultimatum game through heterogeneous
graphs, we investigate the effects of several interference mechanisms which
assume incomplete information and flexible standards of fairness. We quantify
the role of diversity and show how it reduces the need for information
gathering, allowing us to relax a strict, costly interference process.
Furthermore, we find that the influence of certain individuals, expressed by
different network centrality measures, can be exploited to further reduce
spending if minimal fairness requirements are lowered. Our results indicate
that diversity changes and opens up novel mechanisms available to institutions
wishing to promote fairness. Overall, our analysis provides novel insights to
guide institutional policies in socially diverse complex systems.
Related papers
- Social Choice for Heterogeneous Fairness in Recommendation [9.753088666705985]
Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders.
Previous work has often been limited by fixed, single-objective definitions of fairness.
Our work approaches recommendation fairness from the standpoint of computational social choice.
arXiv Detail & Related papers (2024-10-06T17:01:18Z) - Assessing Group Fairness with Social Welfare Optimization [0.9217021281095907]
This paper explores whether a broader conception of social justice, based on optimizing a social welfare function, can be useful for assessing various definitions of parity.
We show that it can justify demographic parity or equalized odds under certain conditions, but frequently requires a departure from these types of parity.
In addition, we find that predictive rate parity is of limited usefulness.
arXiv Detail & Related papers (2024-05-19T01:41:04Z) - Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF [11.43931298398417]
A social choice formulation of the fairness problem offers a flexible and multi-aspect alternative to fairness-aware recommendations.
We show that different classes of choice and allocation mechanisms yield different but consistent fairness / accuracy tradeoffs.
arXiv Detail & Related papers (2023-09-10T17:47:21Z) - FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for
Human-in-the-Loop Systems [8.713442325649801]
We propose a novel algorithm for fairness-aware sequential-decision making in Human-in-the-Loop (HITL) adaptation.
In particular, FAIRO decomposes this complex fairness task into adaptive sub-tasks based on individual human preferences.
We show that FAIRO can improve fairness compared with other methods across all three applications by 35.36%.
arXiv Detail & Related papers (2023-07-12T00:35:19Z) - Causal Fairness for Outcome Control [68.12191782657437]
We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable.
In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision.
We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
arXiv Detail & Related papers (2023-06-08T09:31:18Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Joint Multisided Exposure Fairness for Recommendation [76.75990595228666]
This paper formalizes a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers.
Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation.
arXiv Detail & Related papers (2022-04-29T19:13:23Z) - MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
We study multi-group fairness in machine learning (MultiFair)
We propose a generic end-to-end algorithmic framework to solve it.
Our proposed framework is generalizable to many different settings.
arXiv Detail & Related papers (2021-05-24T02:30:22Z) - Multi-Stage Decentralized Matching Markets: Uncertain Preferences and
Strategic Behaviors [91.3755431537592]
This article develops a framework for learning optimal strategies in real-world matching markets.
We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance.
We prove that participants can be better off with multi-stage matching compared to single-stage matching.
arXiv Detail & Related papers (2021-02-13T19:25:52Z) - Empirical observation of negligible fairness-accuracy trade-offs in
machine learning for public policy [13.037143215464132]
We show that fairness-accuracy trade-offs in many applications are negligible in practice.
We find that explicitly focusing on achieving equity and using our proposed post-hoc disparity mitigation methods, fairness was substantially improved without sacrificing accuracy.
arXiv Detail & Related papers (2020-12-05T08:10:47Z)
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.