Counterfactual Explanation
An explanation of an AI decision that describes what would need to change in the input for the model to produce a different output. It answers 'What if?' questions about predictions.
Why It Matters
Counterfactual explanations are the most actionable form of explainability — they tell users exactly what to change to get a different outcome.
Example
A loan denial explanation: 'Your application would have been approved if your credit score were 680 instead of 640, or if your debt-to-income ratio were below 35%.'
Think of it like...
Like a teacher saying 'You would have passed if you had scored 5 more points on the essay' — it tells you exactly what needed to be different.
Related Terms
Explainability
The ability to understand and articulate how an AI model reaches its decisions or predictions. Explainable AI (XAI) makes the decision-making process transparent and comprehensible to humans.
Interpretability
The degree to which a human can understand the internal mechanisms and reasoning process of a machine learning model. More interpretable models allow deeper inspection of how they work.
Explainable AI
The subfield focused on making AI decision-making processes understandable to humans. XAI techniques provide insights into why a model made a specific prediction.