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”

Explainable AI goes mainstream. But who should be explaining?

Bias in AI is an issue that has really come to the forefront in recent months — our recent blog post discussed the Apple Card/Goldman Sachs alleged bias issue. And this isn’t an isolated instance: Racial bias in healthcare algorithms and bias in AI for judicial decisions are just a few more examples of rampant … Continue reading “Explainable AI goes mainstream. But who should be explaining?”

The never-ending issues around AI and bias. Who’s to blame when AI goes wrong?

We’ve seen it before, we’re seeing it again now with the recent Apple and Goldman Sachs alleged credit card bias issue, and we’ll very likely continue seeing it well into 2020 and beyond. Bias in AI is there, it’s usually hidden, (until it comes out), and it needs a foundational fix. This past weekend we … Continue reading “The never-ending issues around AI and bias. Who’s to blame when AI goes wrong?”