Counterfactual Fairness Assessment

Description

Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's protected attributes (race, gender, age) were different, whilst keeping all other causally legitimate factors constant. The technique requires constructing a causal graph that maps relationships between variables, then using do-calculus or structural causal models to simulate counterfactual scenarios. For example, it asks: 'Would this loan application still be approved if the applicant were a different race, holding constant their actual qualifications and economic circumstances?' This individual-level fairness criterion helps identify when decisions depend improperly on protected characteristics.

Example Use Cases

Fairness

Evaluating a hiring algorithm by testing whether qualified candidates would receive the same evaluation scores if their gender were different, whilst controlling for actual skills, experience, and education, revealing whether gender bias affects recruitment decisions.

Assessing a criminal sentencing model by examining whether defendants with identical criminal histories and case circumstances would receive the same sentence recommendations regardless of their race, identifying potential discriminatory patterns in judicial AI systems.

Limitations

Requires explicit specification of causal relationships between variables, which involves subjective assumptions about what constitutes legitimate versus illegitimate causal pathways.
May be mathematically impossible to satisfy simultaneously with other fairness criteria (like statistical parity), forcing practitioners to choose between competing fairness definitions.
Implementation complexity is high, requiring sophisticated causal inference techniques and structural causal models that are difficult to construct and validate.
Depends heavily on the quality and completeness of the causal graph, which may be incorrect or missing important confounding variables.

Resources

Research Papers

Counterfactual Fairness

Matt J. Kusner et al.•Dec 4, 2017

Counterfactual Fairness in Text Classification through Robustness

Sahaj Garg et al.•Sep 27, 2018

In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different? Toxicity classifiers demonstrate a counterfactual fairness issue by predicting that "Some people are gay" is toxic while "Some people are straight" is nontoxic. We offer a metric, counterfactual token fairness (CTF), for measuring this particular form of fairness in text classifiers, and describe its relationship with group fairness. Further, we offer three approaches, blindness, counterfactual augmentation, and counterfactual logit pairing (CLP), for optimizing counterfactual token fairness during training, bridging the robustness and fairness literature. Empirically, we find that blindness and CLP address counterfactual token fairness. The methods do not harm classifier performance, and have varying tradeoffs with group fairness. These approaches, both for measurement and optimization, provide a new path forward for addressing fairness concerns in text classification.

Software Packages

fairlearn

May 15, 2018

A Python package to assess and improve fairness of machine learning models.

Related Techniques

Name	Description	Assurance Goals
Demographic Parity Assessment	Demographic Parity Assessment evaluates whether a model produces equal positive prediction rates across different demographic groups, regardless of underlying differences in qualifications or base rates. It quantifies fairness using metrics like Statistical Parity Difference (the absolute difference in positive outcome rates between groups) or Disparate Impact ratio (the ratio of positive rates). Unlike techniques that modify data or models, this is purely a measurement approach that highlights when protected groups receive favourable outcomes at different rates, helping organisations identify and document potential discrimination.	Fairness
Sensitivity Analysis for Fairness	Sensitivity Analysis for Fairness systematically evaluates how model predictions change when sensitive attributes or their proxies are perturbed whilst holding other factors constant. The technique involves creating counterfactual instances by modifying potentially discriminatory features (race, gender, age) or their correlates (zip code, names, education institutions) and measuring the resulting prediction differences. This controlled perturbation approach quantifies the degree to which protected characteristics influence model decisions, helping detect both direct discrimination and indirect bias through proxy variables even when sensitive attributes are not explicitly used as model inputs.	Fairness
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
Path-Specific Counterfactual Fairness Assessment	A causal fairness evaluation technique that assesses algorithmic discrimination by examining specific causal pathways in a model's decision-making process. Unlike general counterfactual fairness, this approach enables practitioners to identify and intervene on particular causal paths that may introduce bias whilst preserving other legitimate pathways. The method uses causal graphs to distinguish between direct discrimination (through protected attributes) and indirect discrimination (through seemingly neutral factors that correlate with protected attributes), allowing for more nuanced fairness assessments in complex causal settings.	Fairness Transparency Reliability