Unravelling the (In)compatibility of Statistical-Parity and Equalized-Odds

• URL: http://arxiv.org/abs/2601.19035v1
• Date: Mon, 26 Jan 2026 23:38:57 GMT
• Title: Unravelling the (In)compatibility of Statistical-Parity and Equalized-Odds
• Authors: Mortaza S. Bargh, Sunil Choenni, Floris ter Braak,
• Abstract summary: We study the relations between two types of statistical fairness measures namely Statistical-Parity and Equalized-Odds.<n>The analysis intuitively shows how and when base-rate imbalance causes incompatibility between Statistical-Parity and Equalized-Odds measures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A key challenge in employing data, algorithms and data-driven systems is to adhere to the principle of fairness and justice. Statistical fairness measures belong to an important category of technical/formal mechanisms for detecting fairness issues in data and algorithms. In this contribution we study the relations between two types of statistical fairness measures namely Statistical-Parity and Equalized-Odds. The Statistical-Parity measure does not rely on having ground truth, i.e., (objectively) labeled target attributes. This makes Statistical-Parity a suitable measure in practice for assessing fairness in data and data classification algorithms. Therefore, Statistical-Parity is adopted in many legal and professional frameworks for assessing algorithmic fairness. The Equalized-Odds measure, on the contrary, relies on having (reliable) ground-truth, which is not always feasible in practice. Nevertheless, there are several situations where the Equalized-Odds definition should be satisfied to enforce false prediction parity among sensitive social groups. We present a novel analyze of the relation between Statistical-Parity and Equalized-Odds, depending on the base-rates of sensitive groups. The analysis intuitively shows how and when base-rate imbalance causes incompatibility between Statistical-Parity and Equalized-Odds measures. As such, our approach provides insight in (how to make design) trade-offs between these measures in practice. Further, based on our results, we plea for examining base-rate (im)balance and investigating the possibility of such an incompatibility before enforcing or relying on the Statistical-Parity criterion. The insights provided, we foresee, may trigger initiatives to improve or adjust the current practice and/or the existing legal frameworks.

Related papers

• Detecting Statistically Significant Fairness Violations in Recidivism Forecasting Algorithms [0.0]
  This paper introduces statistical tests that can be used to identify statistically significant violations of fairness metrics.<n>We demonstrate this approach by testing recidivism forecasting algorithms trained on data from the National Institute of Justice.
  arXiv  Detail & Related papers  (2025-09-18T17:15:23Z)
• Targeted Learning for Data Fairness [52.59573714151884]
  We expand fairness inference by evaluating fairness in the data generating process itself.<n>We derive estimators demographic parity, equal opportunity, and conditional mutual information.<n>To validate our approach, we perform several simulations and apply our estimators to real data.
  arXiv  Detail & Related papers  (2025-02-06T18:51:28Z)
• Intrinsic Fairness-Accuracy Tradeoffs under Equalized Odds [8.471466670802817]
  We study the tradeoff between fairness and accuracy under the statistical notion of equalized odds. We present a new upper bound on the accuracy as a function of the fairness budget. Our results show that achieving high accuracy subject to a low-bias could be fundamentally limited based on the statistical disparity across the groups.
  arXiv  Detail & Related papers  (2024-05-12T23:15:21Z)
• Addressing Both Statistical and Causal Gender Fairness in NLP Models [22.75594773147521]
  Statistical fairness stipulates equivalent outcomes for every protected group, whereas causal fairness prescribes that a model makes the same prediction for an individual regardless of their protected characteristics. We demonstrate that combinations of statistical and causal debiasing techniques are able to reduce bias measured through both types of metrics.
  arXiv  Detail & Related papers  (2024-03-30T20:05:41Z)
• Reconciling Predictive and Statistical Parity: A Causal Approach [68.59381759875734]
  We propose a new causal decomposition formula for the fairness measures associated with predictive parity. We show that the notions of statistical and predictive parity are not really mutually exclusive, but complementary and spanning a spectrum of fairness notions.
  arXiv  Detail & Related papers  (2023-06-08T09:23:22Z)
• D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias [57.87117733071416]
  We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases. A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
  arXiv  Detail & Related papers  (2022-08-10T03:41:48Z)
• Measuring Fairness of Text Classifiers via Prediction Sensitivity [63.56554964580627]
  ACCUMULATED PREDICTION SENSITIVITY measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness.
  arXiv  Detail & Related papers  (2022-03-16T15:00:33Z)
• Equality before the Law: Legal Judgment Consistency Analysis for Fairness [55.91612739713396]
  In this paper, we propose an evaluation metric for judgment inconsistency, Legal Inconsistency Coefficient (LInCo) We simulate judges from different groups with legal judgment prediction (LJP) models and measure the judicial inconsistency with the disagreement of the judgment results given by LJP models trained on different groups. We employ LInCo to explore the inconsistency in real cases and come to the following observations: (1) Both regional and gender inconsistency exist in the legal system, but gender inconsistency is much less than regional inconsistency.
  arXiv  Detail & Related papers  (2021-03-25T14:28:00Z)
• Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
  Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
  arXiv  Detail & Related papers  (2020-10-23T19:06:03Z)

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.