Equalised Odds Post-Processing

Description

A post-processing fairness technique based on Hardt et al.'s seminal work that adjusts classification thresholds after model training to achieve equal true positive rates and false positive rates across demographic groups. The method uses group-specific decision thresholds, potentially with randomisation, to satisfy the equalised odds constraint whilst preserving model utility. This approach enables fairness mitigation without retraining, making it applicable to existing deployed models or when training data access is restricted.

Example Use Cases

Fairness

Post-processing a criminal recidivism risk assessment model to ensure equal error rates across racial groups, using group-specific thresholds to achieve equal TPR and FPR whilst maintaining predictive accuracy for judicial decision support.

Transparency

Adjusting a hiring algorithm's decision thresholds to ensure equal opportunities for qualified candidates across gender groups, providing transparent evidence that the screening process treats all demographics equitably.

Reliability

Calibrating a medical diagnosis model's outputs to maintain equal detection rates across age groups, ensuring reliable performance monitoring and consistent healthcare delivery regardless of patient demographics.

Limitations

May require randomisation in decision-making, leading to inconsistent outcomes for similar individuals to achieve group-level fairness constraints.
Post-processing can reduce overall model accuracy or confidence scores, particularly when group-specific ROC curves do not intersect favourably.
Violates calibration properties of the original model, creating a trade-off between equalised odds and predictive rate parity.
Limited to combinations of error rates that lie on the intersection of group-specific ROC curves, which may represent poor trade-offs.
Requires access to sensitive attributes during deployment, which may not be available or legally permissible in all contexts.

Resources

Equality of Opportunity in Supervised Learning

Research Paper•Moritz Hardt, Eric Price, and Nathan Srebro•Oct 7, 2016

Foundational paper by Hardt et al. introducing the equalised odds post-processing algorithm and mathematical framework for fairness constraints.

Equalized odds postprocessing under imperfect group information

Research Paper•Pranjal Awasthi, Matthäus Kleindessner, and Jamie Morgenstern•Jun 7, 2019

Extension of Hardt et al.'s method examining robustness when protected attribute information is imperfect or noisy.

Fairlearn: ThresholdOptimizer

Documentation

Microsoft's Fairlearn implementation of the Hardt et al. algorithm with API documentation and usage examples for equalised odds constraints.

IBM AIF360

Software Package

Comprehensive fairness toolkit including EqualizedOddsPostprocessing implementation based on Hardt et al.'s original algorithm.

Related Techniques

Name	Description	Assurance Goals
Average Odds Difference	Average Odds Difference measures fairness by calculating the average difference in both false positive rates and true positive rates between different demographic groups. This metric captures how consistently a model performs across groups for both positive and negative predictions. A value of 0 indicates perfect fairness under the equalized odds criterion, while larger absolute values indicate greater disparities in model performance between groups.	Fairness Reliability
Fairness GAN	A data generation technique that employs Generative Adversarial Networks (GANs) to create fair synthetic datasets by learning to generate data representations that preserve utility whilst obfuscating protected attributes. Unlike traditional GANs, Fairness GANs incorporate fairness constraints into the training objective, ensuring that the generated data maintains statistical parity across demographic groups. The technique can be used for data augmentation to balance underrepresented groups or to create privacy-preserving synthetic datasets that remove demographic bias from training data.	Fairness Privacy Reliability
Prototype and Criticism Models	Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes represent the most typical instances that best summarise common patterns in the data, whilst criticisms are outliers or edge cases that are poorly represented by the prototypes. For example, in a dataset of customer transactions, prototypes might be the most representative buying patterns (frequent small purchases, occasional large purchases), whilst criticisms could be unusual behaviors (bulk buyers, one-time high-value customers). This dual approach reveals both what is normal and what is exceptional, helping understand data coverage and model blind spots.	Explainability Fairness
Federated Learning	Federated learning enables collaborative model training across multiple distributed parties (devices, organisations, or data centres) without requiring centralised data sharing. Participants train models locally on their private datasets and only share model updates (gradients, weights, or aggregated statistics) with a central coordinator. This distributed approach serves multiple purposes: preserving data privacy and sovereignty, reducing communication costs, enabling learning from diverse data sources, improving model robustness through heterogeneous training, and facilitating compliance with data protection regulations whilst maintaining model performance comparable to centralised training.	Privacy Reliability Safety Fairness
Red Teaming	Red teaming involves systematic adversarial testing of AI/ML systems by dedicated specialists who attempt to identify flaws, vulnerabilities, harmful outputs, and ways to circumvent safety measures. Drawing from cybersecurity practices, red teams employ diverse attack vectors including prompt injection, adversarial examples, edge case exploitation, social engineering scenarios, and goal misalignment probes. Unlike standard testing that validates expected behaviour, red teaming specifically seeks to break systems through creative and adversarial approaches, revealing non-obvious risks and failure modes that could be exploited maliciously or cause harm in deployment.	Safety Reliability Fairness Security
Out-of-DIstribution detector for Neural networks	ODIN (Out-of-Distribution Detector for Neural Networks) identifies when a neural network encounters inputs significantly different from its training distribution. It enhances detection by applying temperature scaling to soften the model's output distribution and adding small, carefully calibrated perturbations to the input that push in-distribution samples towards higher confidence predictions. By measuring the maximum softmax probability after these adjustments, ODIN can effectively distinguish between in-distribution and out-of-distribution inputs, flagging potentially unreliable predictions before they cause downstream errors.	Explainability Reliability Safety