Impact of Fairness Regulations on Institutions' Policies and Population Qualifications
- URL: http://arxiv.org/abs/2404.04534v2
- Date: Sun, 19 May 2024 11:40:42 GMT
- Title: Impact of Fairness Regulations on Institutions' Policies and Population Qualifications
- Authors: Hamidreza Montaseri, Amin Gohari,
- Abstract summary: We consider a system whose primary objective is to maximize utility by selecting the most qualified individuals.
We examine conditions under which a discrimination penalty can effectively reduce disparity in the selection.
We propose certain conditions that can counteract this undesirable outcome.
- Score: 9.863310509852402
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The proliferation of algorithmic systems has fueled discussions surrounding the regulation and control of their social impact. Herein, we consider a system whose primary objective is to maximize utility by selecting the most qualified individuals. To promote demographic parity in the selection algorithm, we consider penalizing discrimination across social groups. We examine conditions under which a discrimination penalty can effectively reduce disparity in the selection. Additionally, we explore the implications of such a penalty when individual qualifications may evolve over time in response to the imposed penalizing policy. We identify scenarios where the penalty could hinder the natural attainment of equity within the population. Moreover, we propose certain conditions that can counteract this undesirable outcome, thus ensuring fairness.
Related papers
- Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints [52.37099916582462]
In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints.
We propose a theoretically guaranteed penalty function method, Exterior Penalty Policy Optimization (EPO), with adaptive penalties generated by a Penalty Metric Network (PMN)
PMN responds appropriately to varying degrees of constraint violations, enabling efficient constraint satisfaction and safe exploration.
arXiv Detail & Related papers (2024-07-22T10:57:32Z) - Fairness-Accuracy Trade-Offs: A Causal Perspective [58.06306331390586]
We analyze the tension between fairness and accuracy from a causal lens for the first time.
We show that enforcing a causal constraint often reduces the disparity between demographic groups.
We introduce a new neural approach for causally-constrained fair learning.
arXiv Detail & Related papers (2024-05-24T11:19:52Z) - Fairness in Algorithmic Recourse Through the Lens of Substantive
Equality of Opportunity [15.78130132380848]
Algorithmic recourse has gained attention as a means of giving persons agency in their interactions with AI systems.
Recent work has shown that recourse itself may be unfair due to differences in the initial circumstances of individuals.
Time is a critical element in recourse because the longer it takes an individual to act, the more the setting may change.
arXiv Detail & Related papers (2024-01-29T11:55:45Z) - "One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviors [57.63649797577999]
We conduct case studies in which we perturb different types of identity-related language features (names, roles, locations, dialect, and style) in NLG system inputs.
We find that motivations for adaptation include social norms, cultural differences, feature-specific information, and accommodation.
In contrast, motivations for invariance include perspectives that favor prescriptivism, view adaptation as unnecessary or too difficult for NLG systems to do appropriately, and are wary of false assumptions.
arXiv Detail & Related papers (2023-10-23T23:00:34Z) - 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) - Social Diversity Reduces the Complexity and Cost of Fostering Fairness [63.70639083665108]
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.
arXiv Detail & Related papers (2022-11-18T21:58:35Z) - Characterization of Group-Fair Social Choice Rules under Single-Peaked
Preferences [0.5161531917413706]
We study fairness in social choice settings under single-peaked preferences.
We provide two separate characterizations of random social choice rules that satisfy group-fairness.
arXiv Detail & Related papers (2022-07-16T17:12:54Z) - Measuring Fairness Under Unawareness of Sensitive Attributes: A
Quantification-Based Approach [131.20444904674494]
We tackle the problem of measuring group fairness under unawareness of sensitive attributes.
We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
arXiv Detail & Related papers (2021-09-17T13:45:46Z) - Distributive Justice and Fairness Metrics in Automated Decision-making:
How Much Overlap Is There? [0.0]
We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority.
We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.
arXiv Detail & Related papers (2021-05-04T12:09:26Z) - Fair Policy Targeting [0.6091702876917281]
One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination.
This paper addresses the question of the design of fair and efficient treatment allocation rules.
arXiv Detail & Related papers (2020-05-25T20:45:25Z)
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.