Professionals worry that AI may take their jobs, but according to TechJury, AI can increase business productivity by 40 percent. The sooner people approach their interactions with computers as a partnership, the closer they can achieve spectacular business results. For AI to maximize its potential, it needs emotional intelligence, and humans also should understand artificial emotional intelligence.

What is Artificial Emotional Intelligence?

Consider this definition from MIT: "Emotion AI is a subset of artificial intelligence (the broad term for machines replicating the way humans think) that measures, understands, simulates, and reacts to human emotion. It's also known as affective computing or artificial emotional intelligence."

When you interact with other humans, you look at their faces, gauge their emotions, and respond accordingly. AI is evolving to do the same thing.

Why is this important? To effectively communicate, a machine needs to take in a person's emotional state and figure out how to respond. Other capabilities they're adapting are recognizing voice inflections and picking up micro-expressions on human faces.

As MIT Sloan Professor Erik Brynjolfsson says, "It makes sense for technology to connect to our social brains, not just our analytical brains. Just like we can understand speech and machines can communicate in speech, we also understand and communicate with humor and other kinds of emotions. And machines that can speak that language - the language of emotions - are going to have better, more effective interactions with us."

Challenges with Artificial Emotional Intelligence

However, artificial emotional intelligence has its complexities and challenges. First, AI software can be biased. As this article in Nature points out:

• AI is known to unfairly judge job applicants because their facial expressions or vocal tones don't match current employees'.
• AI can flag students at schools because their faces seem angry.
• Some facial recognition software interprets black faces as having more negative emotions than white faces.

Secondly, at this point, AI often comes up short when interpreting language ambiguities and cultural differences. For example, it might interpret the idiom "red tape" as actual red-colored Scotch tape. Voice inflection data may cause customer-service agents to treat one type of caller differently than another. A single word can have multiple different meanings, often depending on context. For example, "hot" can indicate temperature or spicy food. Machines also have trouble understanding synonyms, irony, or sarcasm.

Usefulness of Artificial Emotional Intelligence

However, there are also plenty of advantages to artificial emotional intelligence. AI can capture customer emotion and help you leverage it for insight. You can channel these into a mobile or web app design and use the insights to drive critical business decisions.

Other impactful ways to use AI, according to Harvard Business Review, are:

• Gauge employee emotions and how engaged they are. Then you can determine how to keep them happy. For example, some employees may be better off in a different role or project than they are currently doing. AI can match them up to new positions or tasks they may not have previously considered.
• Use customer emotions to drive product design. With capabilities like emotion tracking, AI can keep a finger on the pulse of employees' emotional reactions and responses. For example, AI tools can recognize joy, anger, or drowsiness and adjust the environment accordingly.
• Improve tools that assess employee satisfaction. For example, one tool could detect an upset customer and help a customer service agent respond accordingly.
• Improve learning in general. Schools and universities can use emotional insight to help teachers plan more engaging lessons for different students. For example, a haptic learner may learn differently than a visual learner.

To imbue AI with emotional intelligence to help us, we need to view the computer as a partner. This partnering is sometimes called collaborative intelligence. One approach to the design of a human-machine teaming application may include:

• Train machines to perform specific tasks. This training may include everything from processing data to interaction with humans.
• Understand the outcomes of the tasks, especially when the results are counterintuitive or controversial. This understanding is critical in areas like law enforcement, medicine, insurance, and regulated industries.
• Ensure the responsible use of machines. Organizations may employ safety engineers and others who ensure AI functions properly, safely, and responsibly.

It's also important to maintain ethical standards. Because specific systems may discriminate against different groups of people, Apple has a "differential privacy team." This team helps their AI learn about users without compromising the privacy of individual users.

Conclusion

When they work together, humans and machines have the potential to accomplish great things, both in business and everyday life. However, we have to help them get there. Cultivating artificial emotional intelligence will help machines process information and respond in appropriate ways. Then, we'll be able to make the most of AI's capabilities.