Hello everyone,
Below you'll find some additional resources
I mentioned during the presentation. Please reply with any of your questions below.
Additional Resources:
LIME (Local Interpretable Model-Agnostic Explanations) : https://arxiv.org/abs/1602.04938
Contrastive explanation : https://arxiv.org/abs/1802.07623
IBM Watson OpenScale : https://www.ibm.com/cloud/watson-openscale
IBM AI Fairness 360 Open Source Toolkit : http://aif360.mybluemix.net
IBM AI Explainability 360 Open Source Toolkit: http://aix360.mybluemix.net
Thanks,
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Rakshith Dasenahalli Lingaraju
IBM Data Science and AI Elite
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Original Message:
Sent: Wed April 29, 2020 07:37 PM
From: JORGE CASTANON
Subject: Credit Risk - Why Model Fairness Is Needed: Chat with The Lab Webinar Series
In this webcast, Rakshith will discuss the applications of Machine Learning and AI Fairness techniques in credit risk models for banking institutions. We will cover the typical use cases and the approaches the Data Science Elite (DSE) team used to address the challenges and necessity
in mitigating model bias. Also, we will cover how to increase trust and transparency between the bank and their customers regarding the customer's risk score from the model. This session will also introduce the Credit Risk accelerator that was developed based on the experience of the DSE team.
Credit Risk - Why Model Fairness Is Needed: Chat with The Lab Webinar Series
WATCH NOW
On Demand Recording: here
Check out the Chat with The Lab Webinar Series here
Watch Open Standards for Machine Learning Model Deployment Chat with The Lab Webinar here
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JORGE CASTANON
Chat with labs webinar series: https://ibm.co/Chat-With-The-Lab-Webinar
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