What is the purpose of creating counterfactual explanations in AI testing?

Creating counterfactual explanations serves the purpose of clarifying or justifying AI predictions. These explanations help stakeholders understand why a particular decision was made by the AI system, by showing what changes would lead to a different outcome. For example, if an AI model predicts that a loan application will be declined, a counterfactual explanation might detail what specific changes in the applicant's profile (like a higher income or lower debt) could have led to an approval instead.

This process plays a crucial role in interpreting AI models, which can often be complex and operate as "black boxes." By providing clear scenarios under which different outcomes occur, counterfactual explanations contribute to greater transparency, accountability, and trust in AI systems. Users can better grasp the decision-making process, which is essential for ethical AI governance, particularly in sensitive applications such as finance, healthcare, or legal matters.

While enhancing user interaction, reducing system complexity, and improving visual displays are all valuable aspects of AI systems, they do not specifically address the goal of making AI predictions more understandable and justified, which is the core function of counterfactual explanations.