Group fairness in AI

 Group fairness in AI refers to ensuring that machine learning models treat different demographic groups equitably by achieving parity in statistical measures across groups defined by sensitive attributes like race, gender, or age. This approach evaluates fairness at the population level rather than the individual level16.

Key Principles

• Protected Groups: Groups are defined by sensitive features (e.g., race, gender), which may or may not have privacy implications16.

• Statistical Parity: Requires outcomes to be independent of sensitive attributes. For example, demographic parity mandates equal acceptance rates for job applicants across groups237.

• Equality of Metrics: Common fairness metrics include:

  Metric	Definition
  Equalized Odds	Equal true positive and false positive rates across groups47
  Equality of Opportunity	Equal true positive rates across groups7
  Predictive Parity	Similar precision (positive predictive value) for all groups7

Implementation Challenges

• Trade-offs with Individual Fairness: Group fairness may conflict with individual merit. For instance, enforcing demographic parity in hiring could prioritize underrepresented candidates over more qualified ones to meet statistical targets23.

• Defining Sensitive Features: While some attributes (e.g., race) are obvious, others (e.g., language proficiency) require context-specific analysis16.

• Mathematical Constraints: Fairness is often framed as parity in expectations over the distribution of data, such as $P(\hat{Y}=1 \mid A=a) = P(\hat{Y}=1 \mid A=b)$ for demographic parity, where $\hat{Y}$ is the prediction and $A$ is the sensitive attribute56.

Evaluation

• Confusion Matrix Analysis: Metrics like recall, precision, and accuracy are compared across subgroups4.

• Toolkits: Frameworks like Fairlearn and AI Fairness 360 operationalize group fairness through parity constraints, enabling developers to audit and mitigate biases16.

While group fairness is widely adopted, it has limitations—it cannot address individual-level disparities and may require balancing trade-offs with model accuracy235.

Citations:

1. https://edwinwenink.github.io/ai-ethics-tool-landscape/fairness/group-fairness/
2. https://www.lumenova.ai/blog/group-fairness-vs-individual-fairness/
3. https://fairnessmeasures.github.io/Pages/Definitions
4. https://knowledge.dataiku.com/latest/ml-analytics/responsible-ai/concept-group-fairness.html
5. https://en.wikipedia.org/wiki/Fairness_(machine_learning)
6. https://fairlearn.org/v0.5.0/user_guide/fairness_in_machine_learning.html
7. https://www.brookings.edu/articles/fairness-in-machine-learning-regulation-or-standards/
8. https://haas.berkeley.edu/wp-content/uploads/What-is-fairness_-EGAL2.pdf

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