IEEE P3591 Standard for Fair Decision-Making Through Causal Analysis

Source: standards.ieee.org/ieee/3591/12101/

Description

This standard describes how to perform causal fairness analysis to make fairer decisions in various high-stakes applications (e.g. credit, employment, education) that are more likely to be compliant with a country's antidiscrimination laws and regulations.

Key aspects covered include:

• A standardized fairness model encoding assumptions about causal relationships between variables like protected classes and outcomes
• Standardized language translating concepts across law, causal inference, and machine learning
• Criteria for variable selection in ML models
• Model training and deployment for fairer predictions
• Model evaluation for discrimination likelihood assessment

Note: The standard explicitly focuses on legal compliance and does not cover specific debiasing algorithms.