Demographic Parity Assessment

Description

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.

Example Use Cases

Fairness

Evaluating credit approval algorithms by calculating that loan approval rates for different racial groups must be within 20% of each other (0.8 disparate impact ratio), ensuring compliance with anti-discrimination regulations.

Monitoring hiring platforms by measuring that job recommendation rates for male vs female candidates remain statistically equivalent (Statistical Parity Difference < 0.05), preventing systemic gender bias in career opportunities.

Auditing healthcare triage systems to verify that urgent care assignment rates are equal across ethnic groups, ensuring that automated medical prioritisation doesn't disadvantage minority patients.

Limitations

Purely observational - identifies discrimination but doesn't provide solutions for remediation or bias mitigation.
May penalise models for legitimate differences in base rates between groups, potentially forcing artificial equality where none should exist.
Can conflict with individual fairness principles, where similarly qualified individuals might receive different treatment to achieve group parity.
Doesn't account for quality of outcomes or consider whether equal rates are actually desirable given different group needs or preferences.

Resources

Fairness through awareness

Research Paper•Cynthia Dwork et al.•Apr 20, 2011

AI Fairness 360 Toolkit

Software Package

Fairlearn - Demographic Parity

Documentation

A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

Research Paper•Julia Ive et al.

Related Techniques

Name	Description	Assurance Goals
Counterfactual Fairness Assessment	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.	Fairness
Saliency Maps	Saliency maps are visual explanations for image classification models that highlight which pixels in an image most strongly influence the model's prediction. Computed by calculating gradients of the model's output with respect to input pixels, saliency maps produce heatmaps where brighter regions indicate pixels that, when changed, would most significantly affect the prediction. This technique helps users understand which parts of an image the model is 'looking at' when making decisions.	Explainability Fairness
Neuron Activation Analysis	Neuron activation analysis examines the firing patterns of individual neurons in neural networks by probing them with diverse inputs and analysing their activation responses. This technique helps understand what concepts, features, or patterns different neurons have learned to recognise, providing insights into the model's internal representations. For large language models, this can reveal neurons specialised for linguistic concepts, semantic categories, or even potentially harmful patterns, enabling targeted interventions and deeper model understanding.	Explainability Safety Fairness
Datasheets for Datasets	Datasheets for datasets establish comprehensive documentation standards for datasets, systematically recording creation methodology, data composition, collection procedures, preprocessing transformations, intended applications, potential biases, privacy considerations, and maintenance protocols. These structured documents enhance dataset transparency by providing essential context for appropriate usage, enabling informed decisions about dataset suitability for specific tasks, supporting bias detection and mitigation efforts, ensuring compliance with data protection regulations, and promoting responsible data stewardship throughout the entire data lifecycle from collection to disposal.	Transparency Fairness Privacy
Meta Fair Classifier	An in-processing fairness technique that employs meta-learning to modify any existing classifier for optimising fairness metrics whilst maintaining predictive performance. The method learns how to adjust model parameters or decision boundaries to satisfy fairness constraints such as demographic parity or equalised odds through iterative optimisation. This approach is particularly valuable when retrofitting fairness to pre-trained models that perform well but exhibit bias, as it can incorporate fairness without requiring complete retraining from scratch.	Fairness Transparency Reliability
Fair Transfer Learning	An in-processing fairness technique that adapts pre-trained models from one domain to another whilst explicitly preserving fairness constraints across different contexts or populations. The method addresses the challenge that fairness properties may not transfer when adapting models to new domains with different demographic compositions or data distributions. Fair transfer learning typically involves constraint-aware fine-tuning, domain adaptation techniques, or adversarial training that maintains equitable performance across groups in the target domain, ensuring that bias mitigation efforts carry over from source to target domains.	Fairness Transparency Reliability