The Next Generation of AI: Explainable AI

For most businesses, decisions — from creating marketing taglines to which merger or acquisition to approve — are made solely by humans using instinct, expertise, and understanding built through years of experience. However, these decisions were invariably susceptible to the nuances of human nature: decisions included bias, stereotypes and inconsistent influences. And while humans eventually … Continue reading “The Next Generation of AI: Explainable AI”

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”

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.) Fiddler: Hello everyone, welcome to today’s podcast. My name is Anusha from Fiddler Labs. Today I have … Continue reading “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”