Validating AI models

After periods of hype followed by several AI winters during the past half-century, we are experiencing an AI summer that might be here to stay.  Artificial intelligence promises considerable benefits while the potential challenges to adoption cannot be ignored. In the present paper, we focus on these from a model risk perspective. 

What Will You Learn:

How to upgrade our model risk  framework to deal with AI

What interactive tools exist to increase understanding of algorithms and data

How to monitor ML applications

How to build AI literacy across my organization

About The Author Co-Founder and CEO, Jos Gheerardyn, has built the first FinTech platform that uses AI for real-time model testing and validation on an enterprise-wide scale.

A zealous proponent of model risk governance & strategy, Jos is on a mission to empower risk managers and model validators with smarter tools to turn model risk into a business driver. 

Build better AI applications through improved model risk management

Validating AI models: Principles for managing the risk of AI