Explainable AI for Financial Services
Centelon develops AI based decision systems for financial services. One of the key regulatory requirements is to explain the model to the authorities. We have deployed multiple methods such as Local Interpretable Model Agnostic Explanations (LIME) and Game theory based Shapley Values.
In the talk, I would take the audience through the business case, mathematics behind various explanatory models, design considerations and code demonstration
Outline/Structure of the Workshop
1. Need for explainable AI in Financial Services
2. Two Approaches: Node explanations versus Feature Importance
3. Model specific explanation methods in NLP
4. Model Agnostic Methods a) Drop-one-feature method and its limitation
5. Model Agnostic Methods b) Theoretical background of LIME and its limitations
6. Model Agnostic Methods c) Theoretical background of Shapley Values
7. Equivalence conditions for Shapley Values and LIME
8. Code Demo: ( Financial Services use case, None Financial Services use case)
Learning Outcome
The audience will begin to appreciate the need for building explainable systems. They would learn the basic tool set to deploy in various use cases.
Target Audience
Developers, Executives
Prerequisites for Attendees
Basic Machine Learning, Deep Learning
Links
schedule Submitted 7 months ago
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Recent Activities
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Public Feedback
Dear Prabhash: I was trying to locate your LinkedIn profile and your bio on confengine.com. Could you please add those?
We have two other proposals submitted on a very similar topic and so we are considering those as well.
Warm Regards
Vikas