Challenges – Common pitfalls

We’ve spent a considerable amount of time talking about how to build AI/ML products and use models in a way that empowers your products. We’ve also discussed the hype and commercial excitement about AI. In this section, we’ll temper this hype by understanding why certain AI/ML products fail. We’ll be looking at a few real-world examples that highlight some of the common reasons why AI deployments have received controversy. We will also look into some of the underlying themes within that controversy for new AI products and their creators to try to avoid.

In the following sections, we will focus on challenges associated with ethics, performance, and safety and their accompanying examples.

Ethics

Companies have long struggled with maintaining the quality and ethics of consumer-facing conversational AIs. If you recall back in 2016 when Microsoft unleashed its AI named Tay onto the Twittersphere, it took less than...