Explainable Artificial Intelligence/ Explainable AI is key for any successful implementation at scale.
Here are some of the aspects:
Explanation of Explainable AI
Systems provide the evidence or reason(s) for all outputs.
Meaningful
Systems provide explanations that are understandable to individual users.
Explanation Accuracy
The explanation correctly reflects the system’s process for generating the output.
Knowledge Limits
The system only operates under the conditions for which it was designed or when it reaches sufficient confidence in its output.
IP analysis has always been considered a great use case for AI systems because of the multiple factors involved, such as rich, large text data and several interconnections.
However, for large scale adoption in Industry the following are needed:
- Transparency
- Explainability
- Lack of bias
