Term
Explainable AI (XAI) (ɛks-ˈpleɪ-nə-bl eɪ-ˈaɪ)
Definition
Explainable AI, or XAI, refers to AI systems that enable humans to understand the reasoning behind the decisions these systems make. It adds transparency and trust to AI interactions.
Where you'll find it
In various applications and platforms that use AI, XAI features are often included in settings or documentation. Its visibility might vary depending on the complexity of the AI system and the specific software being used.
Common use cases
- Enhancing user trust by making AI decisions clear and justifiable.
- Compliance with regulations that require transparency in decision-making processes.
- Improving the development and refinement of AI systems by providing insights into how AI conclusions are reached.
Things to watch out for
- Not all AI systems include built-in explainability; some older or more complex models might lack transparency.
- The explanations provided by XAI can sometimes be too technical, depending on the target audience.
- Over-reliance on XAI could lead to neglect in addressing inherent biases in AI models.
Related terms
- Artificial Intelligence
- Machine Learning
- Algorithmic Transparency
- Audit Trails
- Bias in AI
Pixelhaze Tip: When dealing with Explainable AI, always question the "how" and "why" behind the answers it provides. This approach deepens your understanding of the AI's workings and helps you communicate these aspects effectively to others who might rely on your explanation.