CohEx: A Generalized Framework for Cohort Explanation

Cohort explanations fill a critical gap between individual and global model explanations. Most AI explanation techniques focus either on understanding how a model works overall or explaining single decisions, but cohort explanations reveal how models behave for specific groups of people or situations—a middle ground that's particularly important for identifying fairness issues affecting particular populations.

Group-based explanations can reveal hidden biases that individual or global explanations might miss. By examining model behavior across cohorts (such as different demographic groups or customer segments), organizations can detect whether their AI systems make systematically different decisions for specific groups, which is essential for responsible AI deployment.

The framework uses supervised clustering to generate cohort explanations. Rather than treating cohort explanation as a new problem, CohEx leverages existing machine learning techniques (supervised clustering) to systematically identify and explain model behavior patterns within defined groups, making the approach practical for real-world implementation.

Defining "desired properties" for cohort explanations establishes standards for responsible AI. The paper establishes criteria for what makes a good cohort explanation, which helps organizations evaluate whether their explanation methods actually provide meaningful insights into group-level model behavior—important for accountability and trust.

Cohort explanations enable context-specific model auditing. By understanding how models behave differently across specific groups or contexts, organizations can conduct more targeted audits and interventions, moving beyond one-size-fits-all model evaluation toward more nuanced, responsible AI governance.