Structural Interventions and the Dynamics of Inequality

• URL: http://arxiv.org/abs/2406.01323v1
• Date: Mon, 3 Jun 2024 13:44:38 GMT
• Title: Structural Interventions and the Dynamics of Inequality
• Authors: Aurora Zhang, Annette Hosoi,
• Abstract summary: We show that technical solutions must be paired with external, context-aware interventions to enact social change. This research highlights the ways that structural inequality can be perpetuated by seemingly unbiased decision mechanisms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent conversations in the algorithmic fairness literature have raised several concerns with standard conceptions of fairness. First, constraining predictive algorithms to satisfy fairness benchmarks may lead to non-optimal outcomes for disadvantaged groups. Second, technical interventions are often ineffective by themselves, especially when divorced from an understanding of structural processes that generate social inequality. Inspired by both these critiques, we construct a common decision-making model, using mortgage loans as a running example. We show that under some conditions, any choice of decision threshold will inevitably perpetuate existing disparities in financial stability unless one deviates from the Pareto optimal policy. Then, we model the effects of three different types of interventions. We show how different interventions are recommended depending upon the difficulty of enacting structural change upon external parameters and depending upon the policymaker's preferences for equity or efficiency. Counterintuitively, we demonstrate that preferences for efficiency over equity may lead to recommendations for interventions that target the under-resourced group. Finally, we simulate the effects of interventions on a dataset that combines HMDA and Fannie Mae loan data. This research highlights the ways that structural inequality can be perpetuated by seemingly unbiased decision mechanisms, and it shows that in many situations, technical solutions must be paired with external, context-aware interventions to enact social change.

Related papers

• Reconciling Heterogeneous Effects in Causal Inference [44.99833362998488]
  We apply the Reconcile algorithm for model multiplicity in machine learning to reconcile heterogeneous effects in causal inference. Our results have tangible implications for ensuring fair outcomes in high-stakes such as healthcare, insurance, and housing.
  arXiv  Detail & Related papers  (2024-06-05T18:43:46Z)
• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints [51.12047280149546]
  A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints. We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
  arXiv  Detail & Related papers  (2022-12-23T22:29:08Z)
• 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)
• The Equity Framework: Fairness Beyond Equalized Predictive Outcomes [0.0]
  We study fairness issues that arise when decision-makers use models that deviate from the models that depict the physical and social environment. We formulate an Equity Framework that considers equal access to the model, equal outcomes from the model, and equal utilization of the model.
  arXiv  Detail & Related papers  (2022-04-18T20:49:51Z)
• Fair Incentives for Repeated Engagement [0.46040036610482665]
  We study a problem of finding optimal monetary incentive schemes for retention when faced with agents whose participation decisions depend on the incentive they receive. We show that even in the absence of explicit discrimination, policies may unintentionally discriminate between agents of different types by varying the type composition of the system.
  arXiv  Detail & Related papers  (2021-10-28T04:13:53Z)
• Impact Remediation: Optimal Interventions to Reduce Inequality [10.806517393212491]
  We develop a novel algorithmic framework for tackling pre-existing real-world disparities. The purpose of our framework is to measure real-world disparities and discover optimal intervention policies. In contrast to most work on optimal policy learning, we explore disparity reduction itself as an objective.
  arXiv  Detail & Related papers  (2021-07-01T16:35:12Z)
• End-to-End Learning and Intervention in Games [60.41921763076017]
  We provide a unified framework for learning and intervention in games. We propose two approaches, respectively based on explicit and implicit differentiation. The analytical results are validated using several real-world problems.
  arXiv  Detail & Related papers  (2020-10-26T18:39:32Z)
• Beyond Individual and Group Fairness [90.4666341812857]
  We present a new data-driven model of fairness that is guided by the unfairness complaints received by the system. Our model supports multiple fairness criteria and takes into account their potential incompatibilities.
  arXiv  Detail & Related papers  (2020-08-21T14:14:44Z)
• Fair Influence Maximization: A Welfare Optimization Approach [34.39574750992602]
  We provide a principled characterization of the properties that a fair influence algorithm should satisfy. Under this framework, the trade-off between fairness and efficiency can be controlled by a single design aversion parameter. Our framework encompasses as special cases leximin and proportional fairness.
  arXiv  Detail & Related papers  (2020-06-14T14:08:10Z)
• Learning Overlapping Representations for the Estimation of Individualized Treatment Effects [97.42686600929211]
  Estimating the likely outcome of alternatives from observational data is a challenging problem. We show that algorithms that learn domain-invariant representations of inputs are often inappropriate. We develop a deep kernel regression algorithm and posterior regularization framework that substantially outperforms the state-of-the-art on a variety of benchmarks data sets.
  arXiv  Detail & Related papers  (2020-01-14T12:56:29Z)

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.