Term
Interpretability (ˌɪn-tər-prə-tə-ˈbɪl-ɪ-ti)
Definition
Interpretability in AI refers to how easily a person can understand and explain why an AI model makes certain decisions or behaves in a certain way.
Where you’ll find it
Interpretability features can often be found in the analytics or reporting sections of AI platforms, where explanations of how AI models arrive at decisions are displayed. This is especially common in platforms that are used for data science or complex decision-making processes.
Common use cases
- Reviewing AI decisions to ensure they are fair and unbiased.
- Debugging AI models to improve performance or rectify errors.
- Explaining AI model decisions to stakeholders to build trust and understanding.
Things to watch out for
- Some AI models, especially deep learning ones, can be "black boxes," meaning their decision-making processes are not easily interpretable.
- Over-reliance on technical tools for interpretability can overlook the importance of intuitive, human-friendly explanations.
- There can be a trade-off between the complexity of an AI model and its interpretability; more complex models might be less interpretable.
Related terms
- Transparency
- Accountability
- Machine Learning
- AI Ethics
- Model Explainability
Pixelhaze Tip: When working with AI, always check if the platform offers any interpretability tools or features. Using these can help clarify AI decisions, making your projects more transparent and trustworthy to clients.