What is Explainability?
Explainability refers to the ability to clearly understand and interpret how an AI system makes its decisions or predictions. It helps users and developers trust the system by revealing the reasoning behind its outputs in a transparent and human-understandable way.
Table of Contents
Full Definition
Explainability involves techniques and tools that make AI decision processes interpretable.
This transparency is crucial for debugging models, meeting regulatory requirements, and gaining user confidence.
Explainable AI enables stakeholders to validate, challenge, and improve AI-driven outcomes effectively.
Examples
Transparency of AI decisions
Enhanced trust in AI systems
Facilitates compliance with regulations
Benefits
May require additional computational resources
Can be challenging for complex models like deep learning
Needs clear communication for non-technical stakeholders
Common Mistakes
Investing in explainability strengthens AI adoption and accountability.
Conclusion
Investing in explainability strengthens AI adoption and accountability.