Responsible AI With Model Risk Management

The desire among financial institutions to better mitigate risk gained renewed prominence as a result of the financial crisis of 2008. Subsequent regulatory and governance requirements fostered interest in risk modeling and sophisticated forecasting based on artificial intelligence (AI) to improve outcomes. It now seems common to have AI-driven models supporting decision making related to … Continue reading “Responsible AI With Model Risk Management”

CIO outlook 2020: Building an explainable AI strategy for your company

Building an AI strategy needs to include explainability for complete visibility into machine-generated decisions. Explainable AI ensures there’s a human present in the loop of the AI process, where the machine provides transparent and reliable explanations, and the human can correct the machine in cases where its decisions are wrong. The sooner such an AI … Continue reading “CIO outlook 2020: Building an explainable AI strategy for your company”

Latest Explainable AI Newsletter: January 2020

We’re honored to be named a Game Changer 2020 by CB Inisghts. “..identified high-momentum companies pioneering new ways to solve big problems.” Read on CBInsights What we’re reading/watching 3 reasons to open the AI black-box – Dr. Harry Shum ‘If we want to help humans improve AI, we need to understand it. Where are those errors … Continue reading “Latest Explainable AI Newsletter: January 2020”

Explainable AI Podcast: Founder & CTO of Elixr AI, Farhan Shah, discusses AI and the need for transparency

We recently chatted with Farhan Shah, Founder & CTO of Elixr AI and former tech executive at large insurance companies. Take a listen to the podcast below or read the transcript. (Transcript lightly edited for clarity and length.) Listen to all the Explainable AI Podcasts here Fiddler: Hello everyone, welcome to today’s podcast. My name is … Continue reading “Explainable AI Podcast: Founder & CTO of Elixr AI, Farhan Shah, discusses AI and the need for transparency”

How to Design to make AI Explainable

Explainable AI, a topic of research until recently, is now mainstream. Recent research has enabled insights into the behavior of inherently black box AI models that can address its otherwise significant business risks related to bias, compliance and opaque outcomes. However, many platforms and solutions provide these explanations either with flat numbers via API or … Continue reading “How to Design to make AI Explainable”