ai-bias-special-category

Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.

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Repositório: mukul975/Privacy-Data-Protection-Skills
Última atividade na origem: 15 de março de 2026 às 00:40
Idioma detectado do SKILL.md: inglês
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name	ai-bias-special-category
description	Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.
license	Apache-2.0
metadata	{"author":"mukul975","version":"1.0","domain":"privacy","subdomain":"ai-privacy-governance","tags":"ai-bias, special-category, fairness-metrics, discrimination, art-9, data-governance"}

name	ai-bias-special-category
description	Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.
license	Apache-2.0
metadata	{"author":"mukul975","version":"1.0","domain":"privacy","subdomain":"ai-privacy-governance","tags":"ai-bias, special-category, fairness-metrics, discrimination, art-9, data-governance"}

Racial or ethnic origin	Discrimination in hiring, credit, policing	CV screening penalising names associated with ethnic minorities
Political opinions	Political profiling, content suppression	News recommendation amplifying or suppressing political viewpoints
Religious beliefs	Service denial, discriminatory targeting	Insurance pricing varying by religious affiliation
Trade union membership	Employment discrimination	Performance scoring penalising union activity
Genetic data	Genetic discrimination in insurance/employment	Health insurance pricing based on genetic predisposition
Biometric data	Differential accuracy across demographics	Facial recognition with higher error rates for darker skin tones
Health data	Health-based discrimination	Hiring algorithms penalising disability or mental health history
Sexual orientation	Discrimination, outing	Content recommendation inadvertently revealing sexual orientation

Proxy Feature	Inferred Category	Mechanism
Postcode/zip code	Race/ethnicity, income	Residential segregation patterns
First/last name	Race/ethnicity, religion	Name-ethnicity correlations
Browsing history	Political opinion, religion, health	Content consumption patterns
Purchase history	Health status, religion	Medication purchases, dietary products
Language patterns	National origin, education	Dialect, vocabulary, grammar patterns
Device/app usage	Age, income, disability	Accessibility features, device type

Metric	Definition	When to Use
Demographic parity	P(positive outcome	group A) = P(positive outcome
Equalized odds	TPR and FPR equal across groups	When accuracy should be equal across groups
Equal opportunity	TPR equal across groups (relaxed equalized odds)	When true positive detection should be equal
Calibration	P(Y=1	score=s, group=A) = P(Y=1
Predictive parity	PPV equal across groups	When positive predictions should be equally reliable

ai-bias-special-category

Opções de instalação

Revise os arquivos de origem

AI Bias Assessment for Special Category Data

Overview

Art. 9 Special Categories and AI Bias

Direct Processing of Special Category Data

AI Bias Assessment for Special Category Data

Overview

Art. 9 Special Categories and AI Bias

Direct Processing of Special Category Data

Proxy Inference of Special Categories

Fairness Metrics

Group Fairness Metrics

Individual Fairness Metrics

Metric Selection Guidance

Bias Detection Methodology

Phase 1: Data Audit

Phase 2: Model Testing

Phase 3: Output Analysis

Bias Mitigation Strategies

Pre-processing (Data-Level)

In-processing (Algorithm-Level)

Post-processing (Output-Level)

AI Act Art. 10 Data Governance

Documentation Requirements

Bias Assessment Report

Enforcement Precedents

Integration Points

Metric	Definition
Consistency	Similar individuals receive similar outcomes
Counterfactual fairness	Outcome would be the same if protected attribute were different
Causal fairness	No causal path from protected attribute to outcome

Decision Context	Recommended Metric	Justification
Hiring/admissions	Equalized odds or equal opportunity	Equal detection of qualified candidates across groups
Credit scoring	Calibration	Score should mean the same probability regardless of group
Criminal risk	Equalized odds	Both FPR and TPR should be equal to avoid disproportionate impact
Healthcare	Equal opportunity + calibration	Equal detection of conditions; equal meaning of risk scores
Content moderation	Demographic parity	Content removal should not disproportionately affect groups

Strategy	Description	Trade-off
Resampling	Over-sample underrepresented groups, under-sample overrepresented	May reduce data diversity or introduce duplicates
Reweighting	Assign higher weights to underrepresented group samples	Computationally simple; may not address structural bias
Relabelling	Correct historically biased labels	Requires domain expertise; may be subjective
Fair representation learning	Learn latent representation that removes protected attribute information	May lose legitimate correlations

Strategy	Description	Trade-off
Adversarial debiasing	Train adversary to predict protected attribute from model; penalise success	Accuracy-fairness trade-off; requires protected attribute data
Fairness constraints	Add fairness metric as training constraint	May reduce overall accuracy; constraint satisfaction varies
Regularisation	Add fairness-related regularisation term to loss function	Balances accuracy and fairness; requires tuning
Causal modelling	Use causal graph to block discriminatory paths	Requires causal knowledge; complex to implement

Strategy	Description	Trade-off
Threshold adjustment	Different decision thresholds per group to equalise metrics	May be perceived as unfair; legally complex
Score calibration	Calibrate scores per group	Requires sufficient group data; may reduce discrimination
Reject option	Abstain from decision for borderline cases across groups	Reduces coverage; requires human fallback

Requirement	Implementation
Relevant data	Training data must be relevant to the intended purpose
Sufficiently representative	Data must represent the population the system will be deployed on
Free of errors	Data quality assessment and cleaning processes
Complete	Sufficient coverage of deployment scenarios
Appropriate statistical properties	Distribution analysis including demographic representation
Bias examination	Examine training data for possible biases, especially related to Art. 10(2)(f)

Section	Content
System description	Model, purpose, affected groups
Protected attributes assessed	Art. 9 categories + equality law characteristics
Fairness metrics selected	With justification for selection
Data audit results	Training data demographics, representation gaps
Model testing results	Per-group performance, fairness metrics, counterfactual results
Bias findings	Identified disparities with severity assessment
Mitigation measures	Applied strategies with effectiveness evidence
Residual bias	Remaining disparities after mitigation
Trade-off documentation	Accuracy-fairness trade-offs, metric impossibility acknowledgement
Ongoing monitoring plan	Post-deployment fairness monitoring

ai-bias-special-category

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Opções de instalação

Revise os arquivos de origem

Mais deste repositório

Mais deste repositório

AI Bias Assessment for Special Category Data

Overview

Art. 9 Special Categories and AI Bias

Direct Processing of Special Category Data

AI Bias Assessment for Special Category Data

Overview

Art. 9 Special Categories and AI Bias

Direct Processing of Special Category Data

Proxy Inference of Special Categories

Fairness Metrics

Group Fairness Metrics

Individual Fairness Metrics

Metric Selection Guidance

Bias Detection Methodology

Phase 1: Data Audit

Phase 2: Model Testing

Phase 3: Output Analysis

Bias Mitigation Strategies

Pre-processing (Data-Level)

In-processing (Algorithm-Level)

Post-processing (Output-Level)

AI Act Art. 10 Data Governance

Documentation Requirements

Bias Assessment Report

Enforcement Precedents

Integration Points