What does "Factual Explanations" mean?

Table of Contents

• How Factual Explanations Work
• Importance of Factual Explanations
• Relation to Other Types of Explanations

Factual explanations help us understand why a machine, like an image classifier, makes certain decisions. These explanations focus on the actual features of the input data that influenced the machine's choice.

#How Factual Explanations Work

When a machine analyzes an image, it looks at various parts such as colors, shapes, and textures. A factual explanation shows which of these features played a role in making the decision. This helps users know what the machine found important.

#Importance of Factual Explanations

Factual explanations are useful because they provide clarity. Instead of just giving a label or a prediction, they show the reasoning behind it. This makes it easier for people to trust and understand how machines make decisions.

#Relation to Other Types of Explanations

Factual explanations are often compared to counterfactual explanations, which focus on what changes would lead to a different decision. While factual explanations highlight relevant features, counterfactuals show how altering some aspects of the input could change the outcome. Both types are valuable for gaining insights into machine behavior.