Interest is growing in artificial empathy, but so is confusion about what artificial empathy is or needs to be. This confusion makes it challenging to navigate the technical and ethical issues that accompany empathic AI development. Here, we outline a framework for thinking about empathic AI based on the premise that different constellations of capabilities associated with empathy are important for different empathic AI applications. We describe distinctions of capabilities that we argue belong under the empathy umbrella, and show how three medical empathic AI use cases require different sets of these capabilities. We conclude by discussing why appreciation of the diverse capabilities under the empathy umbrella is important for both AI creators and users.

In 2019, a team of researchers published a meta-review of studies claiming a person's emotion can be inferred from their facial movements. They concluded that there's no evidence emotional state can be predicted from expression – regardless of whether a human or technology is making the determination. "[Facial expressions] in question are not'fingerprints' or diagnostic displays that reliably and specifically signal particular emotional states regardless of context, person, and culture," the coauthors wrote. "It is not possible to confidently infer happiness from a smile, anger from a scowl, or sadness from a frown." Alan Cowen might disagree with this assertion.

Most of us have had the experience of sending a text or email that came across sounding insensitive or angry, even though that wasn't our intent. Unfortunately, the lack of social cues in such messaging makes it much easier to be misinterpreted. Depending on the communication, this can lead to misunderstandings, hurt feelings or worse. That's a shortcoming Bellevue, Wash.-based mpathic wants to correct using empathic AI. Drawing on insights and datasets assembled over the past decade, mpathic has set out to promote human connection and understanding in the workplace.

We still have a ways to go before we get to a human equivalent artificial intelligence. You can tell we have a ways to go, because while there's plenty of furor and hype, even the experts are talking hypothetically. What the various prognosticators are really doing, right now, is revisiting the "what if" scenarios associated – scenarios that were imagined, described, and taken to their logical conclusion by science fiction writers thirty and forty years ago. We will know we're getting close to having a true AI when the experts can do more than just wave their hands. Of the two main paths to AI, I doubt the rules-based folks will get us there.