13.1 Fairness in Machine Learning

Fairness in machine learning is crucial for preventing bias and discrimination in AI systems. It involves balancing individual and group fairness, implementing various fairness criteria, and considering ethical implications across different domains like hiring, lending, and criminal justice.

Addressing fairness in ML requires tackling biases in data collection, processing, and algorithms. Techniques include pre-processing data, modifying learning algorithms, and post-processing model outputs. Evaluating fairness involves metrics like demographic parity and intersectional analysis to ensure ethical AI deployment.

Fairness in Machine Learning

Defining Fairness in ML Context

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  Fairness Definitions Explained (Research Summary) | Montreal AI Ethics Institute View original

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• Fairness in machine learning prevents bias or discrimination against individuals or groups based on protected attributes (race, gender, age, socioeconomic status)
• Encompasses individual fairness (treating similar individuals similarly) and group fairness (ensuring equal treatment or outcomes across demographic groups)
• Crucial to prevent perpetuating or amplifying existing societal biases and ensure ethical AI system deployment
• Multiple definitions address different aspects of fairness
  • Demographic parity
  • Equal opportunity
  • Equalized odds
• Implementing fairness involves trade-offs between fairness criteria and model performance
• Extends beyond technical issues to legal and ethical concerns with implications for social justice, equality, and human rights
• Impacts various domains with significant real-world consequences (hiring, lending, criminal justice, healthcare)

Fairness Considerations in ML Applications

• Fairness implementation requires careful consideration of specific context and application
• Balancing fairness criteria often necessitates compromises in model accuracy or other performance metrics
• Ethical implications of fairness decisions must be thoroughly evaluated
• Legal considerations vary by jurisdiction and application domain
• Stakeholder engagement helps ensure fairness aligns with societal values and expectations
• Continuous monitoring and adjustment of fairness measures adapts to changing societal norms and emerging biases
• Transparency in fairness implementation builds trust and accountability in ML systems

Bias in ML Models and Datasets

Types of Bias in Data Collection and Processing

• Data collection bias skews predictions for underrepresented groups due to non-representative training data
• Historical bias reflects past societal prejudices in training data, perpetuating discriminatory patterns
• Measurement bias occurs when features or labels inaccurately capture underlying phenomena for all groups
• Sampling bias results from non-random data point selection, potentially misrepresenting subgroups
• Labeling bias emerges when human annotators introduce biases while annotating training data
• Examples of biased datasets:
  • Facial recognition systems trained primarily on light-skinned individuals
  • Natural language processing models trained on text from limited demographic sources

Algorithmic and Proxy Bias

• Algorithmic bias arises from design choices and assumptions in ML algorithm development
• Favors certain groups over others due to inherent model structure or optimization criteria
• Proxy bias occurs when neutral features indirectly act as proxies for protected attributes
• Examples of proxy variables:
  • Zip code as a proxy for race or socioeconomic status
  • Job title as a proxy for gender in salary prediction models
• Identifying and mitigating proxy bias requires careful feature analysis and selection
• Algorithmic bias mitigation techniques:
  • Adversarial debiasing
  • Fairness-aware regularization
  • Causal modeling to identify and remove unfair dependencies

Mitigating Bias in ML Systems

Pre-processing and In-processing Techniques

• Pre-processing techniques modify training data to reduce bias
  • Resampling balances representation across groups
  • Reweighting assigns higher importance to underrepresented samples
  • Generating synthetic data augments underrepresented groups
• In-processing methods modify learning algorithms to enforce fairness constraints
  • Adversarial debiasing introduces a discriminator to remove protected information
  • Fairness-aware regularization adds fairness terms to the loss function
  • Constrained optimization enforces fairness criteria during model training
• Implementing fairness through awareness incorporates protected attributes into the model
• Counterfactual fairness ensures consistent predictions when changing only protected attributes

Post-processing and Ensemble Methods

• Post-processing approaches adjust model outputs after training
  • Threshold adjustment modifies decision boundaries to achieve fairness
  • Calibrated equalized odds adjusts predictions to equalize error rates across groups
• Ensemble methods combine multiple models with different fairness objectives
  • Weighted averaging of models optimized for different fairness criteria
  • Stacking models trained with various bias mitigation techniques
• Causal inference techniques identify and mitigate unfair causal relationships
  • Counterfactual reasoning to assess the impact of protected attributes
  • Causal graphs to visualize and intervene on biased pathways in the model

Evaluating Fairness in ML Models

Fairness Metrics and Their Applications

• Demographic parity measures equal probability of positive outcomes across protected groups
  • Formula: $P(\hat{Y}=1|A=a) = P(\hat{Y}=1|A=b)$ for all protected attribute values a and b
• Equal opportunity assesses equal true positive rates across different protected groups
  • Formula: $P(\hat{Y}=1|Y=1,A=a) = P(\hat{Y}=1|Y=1,A=b)$ for all a and b
• Equalized odds requires equal true positive and false positive rates across protected groups
  • Extends equal opportunity to include $P(\hat{Y}=1|Y=0,A=a) = P(\hat{Y}=1|Y=0,A=b)$
• Disparate impact ratio compares proportion of positive outcomes between groups
  • Formula: $\frac{P(\hat{Y}=1|A=a)}{P(\hat{Y}=1|A=b)}$ with a threshold (often 80%) for unfair disparities

Advanced Fairness Evaluation Techniques

• Intersectional fairness evaluation considers combined effects of multiple protected attributes
  • Addresses compounded disadvantages (gender and race interactions)
• Qualitative analysis captures nuanced fairness aspects not reflected in numerical measures
  • Expert review of model decisions and their societal implications
  • User studies to assess perceived fairness of model outputs
• Cross-validation and bootstrapping assess stability of fairness metrics
  • K-fold cross-validation to evaluate fairness across different data subsets
  • Bootstrapping to estimate confidence intervals for fairness metrics
• Fairness evaluation in dynamic environments
  • Time-series analysis of fairness metrics to detect drift over time
  • A/B testing to compare fairness of different model versions in production