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Feature


EMOTION AI’S RISKS AND REWARDS: 4 TIPS TO USE IT RESPONSIBLY

Sharon Goldman@sharongoldman
April 28, 2022 7:10 AM
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Over the past two weeks, emotions have run high around the evolution and use of
emotion artificial intelligence (AI), which includes technologies such as
voice-based emotion analysis and computer vision-based facial expression
detection. 

Video conferencing platform Zoom came under fire after saying it might soon
include emotion AI features in its sales-targeted products. A nonprofit advocacy
group, Fight for the Future, published an open letter to the company: It said
Zoom’s possible offering would be a “major breach of user trust,”  is
“inherently biased,” and “a marketing gimmick.” 

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Meanwhile, Intel and Classroom Technologies are working on tools that use AI to
detect the mood of children in virtual classrooms. This has led to media
coverage with unfortunate titles such as “Emotion-Tracking Software Could Ding
Your Kid for Looking Bored in Math.” 

Finally, Uniphore, a conversational AI company with headquarters in Palo Alto,
California and India, is enjoying unicorn status after announcing $400 million
in new funding and a $2.5 billion valuation back in February. In January 2021,
the company acquired Emotion Research Lab, which uses “advanced facial emotion
recognition and eye-tracking technology to capture and analyze interactions over
video in real-time to enhance engagement between people.”


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Last month, it introduced its Q for Sales solution, which “leverages computer
vision, tonal analysis, automatic speech recognition and natural language
processing to capture and make recommendations on the full emotional spectrum of
sales conversations to boost close rates and performance of sales teams.” 

But computer scientist and famously fired, former Google employee, Timnit Gebru,
who founded an independent AI ethics research institute in December 2021, was
critical of Uniphore’s claims on Twitter.  “The trend of embedding pseudoscience
into ‘AI systems’ is such a big one,” she said.  

What does this kind of pushback mean for the enterprise? How can organizations
calculate the risks and rewards of investing in emotion AI? Experts maintain
that the technology can be useful in specific use cases, particularly when it
comes to helping customers and supporting salespeople.


COMMITMENT TO TRANSPARENCY IS KEY

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But, they add, an emotion AI investment requires a commitment to transparency.
Organizations also need a full understanding about what the tools can and can’t
do, as well as careful consideration around potential bias, data privacy and
ROI. 

Today’s evolving emotion AI technologies “may feel a little bit more invasive,”
admitted Annette Zimmerman, a vice president and analyst at Gartner who
specializes in emotion AI. “For the enterprise, I think transparency needs to be
the top priority.” In December 2021, Zimmerman published a Gartner Competitive
Landscape report for the emotion AI space. She pointed out that since the
pandemic, organizations are “seeking to add more empathy in customer
experiences.” 

However, organizations also need to be sure the technology works and that the
system is trained in a way that there is no bias introduced, she told
VentureBeat. “For example, computer vision is very good at detecting obvious
emotions like happiness and deep frustration,” she explained. “But for more
subtle things like irony, or slightly annoyed versus very angry, the model needs
to be trained, particularly on geographic and ethnic differences.”


EMOTION AI COULD BECOME KEY DIFFERENTIATOR

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Zimmerman, who highlighted Uniphore in her competitive landscape report, wrote
that combining computer vision and voice-based emotion analytics “could become a
key differentiator for the company.” 

In an emailed comment to VentureBeat, Patrick Ehlen, vice president of AI at
Uniphore, said, “it’s important to call out that meeting recordings and
conversational intelligence applications have become mainstream in today’s
business world.” The company’s intent with Q for Sales, he continued, “is to
make virtual meetings more engaging, balanced, interactive and valuable for all
parties.” 

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There are a few ways “we ensure there is no creepiness,” he added. “We ask for
consent before the call begins, we don’t profile people on calls and we don’t
perform facial ID or facial recognition.” In addition, he explained, all
participants have the choice to opt-in rather than just opt-out with complete
two-party consent at the beginning of each video meeting. 

Ehlen also wanted to address “confusion about whether we are claiming to have
developed AI that ‘detects emotions’ or knows something about people’s internal
emotional states.” This is not Uniphore’s claim at all, he said: “Rather, we are
reading the signals people sometimes use to communicate about their emotions,
using combinations of facial expressions and tone of voice, for example.” For
example, he explained, the phrase ‘Nice day, isn’t it?’ “might appear to
communicate one thing if you only consider the text by itself, but if it comes
with a sarcastic tone of voice and a roll of the eyes, this communicates
something else.” 


AI-DRIVEN EMOTIONAL ANALYSIS IS INCREASINGLY SOPHISTICATED

Sentiment analysis for text and voice has been around for years: Any time you
call a customer service line or contact center and hear “this call is being
recorded for quality assurance,” for example, you’re experiencing what has
become highly sophisticated, AI-driven conversational analysis. 

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Zimmerman also highlighted Boston-based Cogito in Gartner’s Competitive
Landscape as “a pioneer in audio-based emotion AI technology, providing
real-time emotion analytics for call agent support/coaching, as well as
stress-level monitoring.”

The company first provided AI solutions to the U.S. Department of Veteran
Affairs – to analyze the voices of military veterans with PTSD to determine if
they need immediate help. Then, they moved into the contact center space with an
AI-driven sentiment analysis system that analyzes conversations and guides
customer service agents in the moment. 

“We offer real-time guidance in understanding how the call is going and the
caller’s psychological state,” said Josh Feast, CEO of Cogito. “For instance,
what’s the experience like for the parties on the call? What are fatigue levels?
How is receptivity or motivation?” 

Then, the solution provides the agent with specific cues, perhaps advising them
to adjust the conversation pitch or speed. Or, it could provide recognition that
the other party is distressed. “That provides an opportunity to show some
empathy,” he said. 


WHAT ENTERPRISES NEED TO KNOW BEFORE INVESTING IN EMOTION AI

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GIVE EMOTION AI C-LEVEL ATTENTION

“Executives need to know that emotion AI has great possibilities along with
great responsibilities,” said Theresa Kushner, data and analytics practice lead
at NTT DATA Services. “Managing these complicated AI algorithms is something
that needs C-level attention and can’t be delegated to data scientist teams or
to operations staff. They’ll need to understand the level of commitment that
implementing and operationalizing a controversial technology such as emotion AI
requires and be closely involved to ensure it doesn’t get out of hand.”


CONSIDER THE ROI

When talking to different vendors, make sure they really demonstrate the ROI,
said Zimmerman: “You need to understand the benefit of investing in this
particular technology – does it help me to increase customer satisfaction? Or
does it help me to increase retention and reduce churn?” Uniphore’s Ehlen added
that organizations should also look for a solution that can bring an immediate
ROI. “Solutions in this realm should be able to help augment human interactions
in real time and then become more intelligent and bespoke over time,” he
explained. 

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UNDERSTAND THE ALGORITHM AND DATA COLLECTION 

Questions about data collection and integration with other vendor solutions
should always be top of mind, said Kushner, while when it comes to emotion AI
specifically, organizations should make sure the technology doesn’t violate any
of their ethical boundaries. “Consider asking if they can explain the AI
algorithm that generates this emotional response? What data do they use for the
emotional side of emotion AI? How is it collected? What will we have to collect
to enrich that dataset?” It’s also important to understand the technology’s real
capabilities and limitations, Ehlen added: “Is it single mode or multimode AI?
Siloed or fused? This will determine the level of context and accuracy that you
can eventually derive.” 


IMPLEMENT A TEST AND LEARN FRAMEWORK 

These days, emotion AI technology has evolved to the point that organizations
are deploying large-scale projects. “That requires thinking carefully about
change management, setting up a steering committee and, critically, implementing
some type of test and learn framework,” Feast said, which can lead to new use
case ideas. “For example, we have customers who tested our technology to give
agents real-time guidance, but they also realized they could use it to signal
when agents are getting tired and need a break.” 

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BALANCING EMOTION AI’S RISKS AND REWARDS

According to Gartner’s Zimmerman, emotion AI technology adoption still has a
long way to go, particularly when it comes to Big Tech. “I assumed that, given
some of the technology advances that Amazon has revealed and some discussions
that Google has had, that many more devices would have this functionality, but
they can’t. I think from a technology perspective they could do it, but maybe it
is the privacy issues.” 

Enterprise customers, too, have to weigh the risks and rewards of emotion AI.
Kushner points out that a business may think they’d want to know how a customer
really feels about their interaction with an online call center and employ
emotion AI technology to find out. “But this risks alienating a customer if the
emotion AI technology didn’t represent the customer’s feelings appropriately and
customer support responds in a way that doesn’t fit the emotion the customer had
expressed,” she said. 

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To strike the right balance, said Uniphore’s Ehlen, vendors and customers alike
need to build on trust, which, in turn, is built on open communication and
choice. “We are openly addressing what our solution can do and being clear on
what it cannot do,” he said. “We are giving customers the choice to integrate
this tool into their engagements or not. For those who do opt in, we follow
industry best practices for data privacy and protection.” 

The bottom line, said Feast, is that to succeed with emotion AI, enterprises
need to make the technology use a win-win-win: “With every use case, I think
organizations need to ask themselves ‘Is it good for the enterprise? Is it good
for employees? Is it good for consumers?”

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