[XAI] Explainable AI in Retail | AISC

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=SFa8lzMZ_bw


Duration: 57:06
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Speaker(s): Andrey Sharapov
Facilitator(s): Ali El-Sharif

Find the recording, slides, and more info at https://ai.science/e/xai-explainable-ai-in-retail--P5GX9My5lrZufnFvL9h6

Motivation / Abstract
Andrey will review approaches and tools for explaining ML models along with a retail use case.

What was discussed?
1) Some of these explainability methods, like LIME, are statistical methods that suffer from instability, giving a different explanation for the same prediction. Did you encounter this issue in your practice, and if yes, how have you addressed it?
2) You showed us a number of tools, do you typically run them all? or do you run a few? Which top three tools do you did find useful?
3) If you run multiple explainer tools and get different or contradicting explanations, what is your typical next step?
4)Have you had cases in which the explanation triggered you to go back and make changes to the model? If yes, is there any example you could share?


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