Artificial empathy in AI goes beyond simply recognizing whether a person appears happy, angry, or frustrated. For a machine to respond empathetically, it must interpret emotional signals within their broader context and then adjust its behavior accordingly. That represents a significantly more complicated challenge than traditional pattern recognition.
Advances in machine learning have already made it possible to analyze signals such as facial expressions, speech, tone, gestures, and body language. Applications such as emotionally responsive ATMs demonstrate how this technology could eventually influence financial services, but they also raise questions about where artificial emotional intelligence can provide the greatest practical value.
This article in Information Week is a fun look at how effectively machine learning can become empathetic. The system must not only recognize the user’s emotions but also change how it interacts with the user based on those emotions:
“Merriam Webster’s primary definition of empathy is:
“The action of understanding, being aware of, being sensitive to and vicariously experiencing the feelings, thoughts and experience of another of either the past or present without having the feelings, thoughts and experience fully communicated in an objectively explicit manner; also: the capacity for this.”
To achieve artificial empathy, according to this definition, a machine would have to be capable of experiencing emotion. Before machines can do that, they must first be able to recognize emotion and comprehend it.
Non-profit research institute SRI International and others have succeeded with the recognition aspect, but understanding emotion is more difficult. For one thing, individual humans tend to interpret and experience emotions differently.
“We don’t understand all that much about emotions to begin with, and we’re very far from having computers that really understand that. I think we’re even farther away from achieving artificial empathy,” said Bill Mark, president of Information and Computing Services at SRI International, whose AI team invented Siri. “Some people cry when they’re happy, a lot of people smile when they’re frustrated. So, very simplistic approaches, like thinking that if somebody is smiling they’re happy, are not going to work.”
Emotional recognition is an easier problem to solve than emotional empathy because, given a huge volume of labeled data, machine learning systems can learn to recognize patterns that are associated with a particular emotion. The patterns of various emotions can be gleaned from speech (specifically, word usage in context, voice inflection, etc.), as well as body language, expressions and gestures, again with an emphasis on context. Like humans, the more sensory input a machine has, the more accurately it can interpret emotion.
Recognition is not the same as understanding, however. For example, computer vision systems can recognize cats or dogs based on labeled data, but they don’t understand the behavioral characteristics of cats or dogs, that the animals can be pets or that people tend to love them or hate them.
Similarly, understanding is not empathy. For example, among three people, one person may be angry, which the other two understand. However, the latter two are not empathetic: The second person is dispassionate about the first person’s anger and the third person finds the first person’s anger humorous.”
The Information Week article even has an example of a bank that implemented an ATM with emotional recognition technology:
“Neither AI nor emotions are one thing. Similarly, there is not just one use case for artificial emotional intelligence, be it emotional recognition, emotional understanding or artificial empathy.
“The actual use case matters,” said Strier. “Depending on the context, it’s going to be super powerful or maybe not good enough.”
At the present time, a national bank is piloting a smart ATM that uses a digital avatar which reads customers’ expressions. As the avatar interacts with customers, it adapts its responses.
‘We can now read emotions in many contexts. We can interpret tone, we can we can triangulate body language and words and eye movements and all sorts of proxies for emotional state. And we can learn over time whether someone is feeling this or feeling that. So now the real question is what do we do with that?’ said Strier. ‘Artificial empathy changes the art of the possible, but I don’t think the world quite knows what to do with it yet. I think the purpose question is probably going to be a big part of what going to occupy our time.’ ”
I would suggest that the bank first utilize machine learning and sensor technology to recognize when criminals are modifying the ATM to capture PIN or card data. After that, they can develop the technology that will make me feel like that ATM is my friend.
Artificial empathy in AI remains a developing concept because recognizing an emotion is fundamentally different from understanding or responding appropriately to it. Human emotions are complex, contextual, and highly individual, making simple interpretations of expressions or behavior insufficient.
For financial institutions, emotional recognition could eventually help create more responsive customer experiences, including ATMs and other automated service channels that adapt their interactions based on customer behavior. However, machine learning and sensor technology also have more immediate applications in areas such as security and fraud prevention. Determining where these capabilities provide meaningful value may ultimately be as important as improving the technology itself.
Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group
Read the quoted story here