As a Data guys, I see counterfactual explanations as a powerful way to make AI decisions more transparent and actionable. Instead of just showing why a model made a certain prediction, counterfactuals help us understand how things could change to reach a different outcome. For example, if a loan application was rejected, the system might indicate that increasing annual income by $10,000 or improving the credit score by 50 points could have led to approval.

This kind of insight bridges the gap between complex machine learning models and practical business understanding. Unlike traditional explanation methods that focus on internal model behaviour or feature importance, counterfactuals empower users with clear, data-driven guidance on what actions can actually influence outcomes. It’s a meaningful step toward responsible AI — helping organizations make fairer, more explainable, and more user-centric decisions.