Term
Explainability (eks-ʌ-pləɪ-na-bɪl-ə-tee)
Definition
Explainability in AI refers to how easily and clearly humans can understand the way an AI system makes decisions. It is essential for ensuring that users can trust and effectively manage AI tools.
Where you'll find it
Explainability features are usually found in the model interpretation or explainability sections of an AI platform’s menu. This feature may vary by platform and is often included in tools aimed at sectors such as healthcare and finance.
Common use cases
- In healthcare AI, to understand why a machine suggests a particular diagnosis or treatment.
- In finance, to interpret how AI decides on loan approvals or risk assessments.
- In customer service, to trace how chatbots or virtual assistants formulate responses.
Things to watch out for
- Not all AI platforms fully support explainability, which can limit understanding in critical applications.
- Explainability can be technically complex, making it difficult for non-experts to fully grasp.
- Some users may find explanations too simple or too complex, leading to confusion or misunderstanding.
Related terms
- Transparency
- Model Interpretation
- Decision Path
- AI Ethics
- Trustworthiness