AI BriefingAI, Did the Result You Produced Really Match Your Hypothesis?: A Guide to Using XAI
Key point
This defines the concept of Explainable AI (XAI) and introduces use cases for improving the performance and user experience of Computer Vision and NLP models.
Details
Machine learning models are composed of complex algorithms, making it difficult for humans to understand the process by which results are derived. To address this black box problem, XAI (eXplainable Artificial Intelligence), or explainable artificial intelligence, is drawing attention.
An AI algorithm is like a function that takes an input and produces an output. XAI explains the correlation between the input and output of this function, primarily by analyzing the contribution of the input to the output to provide grounds for the result.
Actual use cases are as follows.
- Facial age prediction (Computer Vision): By visualizing which specific areas (e.g., the tip of the nose, the shape of the mouth) influenced the age prediction, it is possible to go beyond simply delivering results to improve user experience, such as by recommending customized care products.
- Sentiment analysis (NLP): By checking whether specific words (e.g., 'today,' 'indeed') excessively contribute to sentiment classification, this can be used as grounds for improving model performance, such as by checking model bias and revising tokenization strategies.
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