Google Solution Challenge 2026

Auditing ML Systems
for True Fairness

Quantify bias across sensitive attributes, trace predictions through SHAP explainability, and apply state-of-the-art mitigation algorithms—all with compliance-ready reporting.

DEMOGRAPHIC PARITYEQUALIZED ODDSEQUAL OPPORTUNITYSTATISTICAL PARITYSHAP VALUESFEATURE ATTRIBUTIONBIAS MITIGATIONDISPARATE IMPACT80% RULEADULT INCOMECOMPAS RECIDIVISMGERMAN CREDITPROXY DETECTIONCOVARIATE SHIFTLABEL BIASMODEL EXPLAINABILITYCOMPLIANCE AUDITNIST FRAMEWORKEU AI ACTISO 27001SOC2 TYPE IIFAIRLEARNSCIKIT-LEARNGEMINI 2.0 FLASHFASTAPINEXT.JSDEMOGRAPHIC PARITYEQUALIZED ODDSEQUAL OPPORTUNITYSTATISTICAL PARITYSHAP VALUESFEATURE ATTRIBUTIONBIAS MITIGATIONDISPARATE IMPACT80% RULEADULT INCOMECOMPAS RECIDIVISMGERMAN CREDITPROXY DETECTIONCOVARIATE SHIFTLABEL BIASMODEL EXPLAINABILITYCOMPLIANCE AUDITNIST FRAMEWORKEU AI ACTISO 27001SOC2 TYPE IIFAIRLEARNSCIKIT-LEARNGEMINI 2.0 FLASHFASTAPINEXT.JS

FrameworkFairlearn + Scikit-Learn

ExplainabilitySHAP Integration

BackendFastAPI High-Performance

IntelligenceGemini 2.0 Flash

ComplianceEU AI Act · NIST

Enterprise Capabilities

Core analysis modules designed for evaluating, explaining, and improving model fairness at scale.

Demographic Parity

Measures outcome distribution across protected groups to quantify selection rate disparities.

Equalized Odds

Compares true positive and false positive rates across demographic groups.

SHAP Attribution

Identifies which input features drive model predictions using Shapley values.

Proxy Detection

Detects features acting as proxies for protected attributes through correlation analysis.

Bias Mitigation

Applies pre-processing and in-processing techniques to reduce disparities.

Compliance Reports

Generates structured audit reports mapped directly to the EU AI Act and NIST AI RMF standards.

The Audit Pipeline

A rigorous, end-to-end workflow that transforms raw data into a mitigated, compliance-ready model.

Phase 01

Data Integration

Seamlessly upload your CSV or connect to established benchmark datasets. Built for large-scale tabular data.

Data Module

Phase 02

Attribute Mapping

Intelligently identify and map target variables, features, and sensitive attributes to prepare for deep fairness analysis.

Attribute Module

Phase 03

Bias Evaluation

Quantify disparities across protected groups using industry-standard metrics like Demographic Parity and Equalized Odds.

Bias Module

Phase 04

Scenario Simulation

Explore the fairness-accuracy tradeoff dynamically by adjusting decision thresholds and observing real-time metric updates.

Scenario Module

Phase 05

SHAP Explainability

Uncover the "why" behind model predictions. Identify exactly which features are driving biased outcomes using Shapley values.

SHAP Module

Phase 06

Algorithmic Mitigation

Apply advanced pre-processing and in-processing techniques from Fairlearn to mathematically reduce bias while preserving accuracy.

Algorithmic Module

Phase 07

AI Consultant

Interact with our context-aware assistant to interpret audit findings, explore mitigation strategies, and get actionable recommendations.

AI Module

Phase 08

Compliance Reporting

Generate comprehensive executive summaries and detailed technical reports, fully exportable as PDFs for stakeholders.

Compliance Module

Rigorous Methodology

Built on established research in algorithmic fairness and model interpretability.

Bias Detection & Mitigation

Fairlearn Framework

Powered by Microsoft Research's open-source toolkit. We leverage its robust implementations of Demographic Parity, Equalized Odds, and advanced mitigation algorithms.

Read Documentation

Model Explainability

SHAP Integration

Unpacking the black box with Shapley Additive Explanations. By attributing exact contributions to each feature, auditors can pinpoint precisely where bias originates.