A Survey on Interpretability in Visual Recognition

Visual recognition AI systems need interpretability for safety-critical applications: The paper emphasizes that as these models are increasingly deployed in high-stakes domains like autonomous driving and medical diagnostics, understanding how and why they make decisions has become essential rather than optional.

Interpretability requires a human-centered approach: The survey establishes that effective explanations must consider multiple dimensions—including the intent behind the explanation, what aspect is being explained, how it's presented to users, and the methodology used—rather than applying one-size-fits-all solutions.

Visual recognition explanations bridge vision and language: Because humans understand the world through both seeing and language, the most effective AI explanations combine visual information with natural language descriptions, creating a more intuitive way for people to understand model decisions.

There are measurable standards for evaluating explanations: The paper identifies critical evaluation metrics and benchmarks for assessing whether explanations are actually helpful and trustworthy, suggesting that "interpretability" isn't subjective but can be rigorously tested.

Multimodal AI systems present new interpretability challenges and opportunities: As AI models increasingly combine multiple types of information (images, text, etc.), new approaches to explanation are emerging, indicating that interpretability research must evolve alongside advancing AI capabilities.