Natural language processing will help humans and machines have more empathy

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With the current breakneck pace of innovation, it may seem like technology is on an inevitable and aggressive path to solving all of humanity’s most pressing problems.

And in some respects, we’re making great progress. We’ve broken tremendous ground in such areas as renewable energy, disease prevention, and disaster recovery. But when it comes to addressing important human-centric challenges—workforce diversity, unconscious bias, or employee and customer satisfaction—technology has a long history of coming up short.

That’s because solving technical problems like jet propulsion or GPS is largely math and physics related, where computers (and programmers) excel. But solving human-centric problems like employee engagement usually requires empathy, and that’s notoriously hard to codify. Humans are emotional creatures, especially when it comes to making decisions. First we feel, then we apply logic to help justify our emotional response, and, finally, we act. Thus, any attempt to help people make better decisions that don’t account for our emotions is almost always destined to fail.

However, with recent advances in artificial intelligence and especially natural language processing (NLP), we finally have the technological tools for tapping into the power and complexity of human feelings. This approach has major implications for how we design systems, and it’s leading to solutions with more humanistic points of view.

Programming for difference

Language is incredibly complex. From one person to the next, the slightest difference in someone’s experience or environment can shape how they express themselves. Dialects, genders, locations, and even seasons all can change the words we use to convey the same meaning.

People are very good at accounting for these subtle differences. However, for computers, it’s a massive challenge. To come anywhere close to achieving human-level understanding, they need an enormous, and rich set of language training spanning countless examples of each variation of demographics, experiences, and backgrounds.

To see how this works in real life, just think about a teenager in California using the word “lit” (meaning “exciting”) when reviewing a new smartphone, and what that same word might mean in a review coming from a senior citizen in Massachusetts (perhaps “screen brightness”).

Reading between the lines

For the first time, we’re able to teach computers to understand not only the basics of what people are saying—like counting the words or looking for specific phrases—but to intelligently “read between the lines” and get to the true intent and meaning behind our words. This, of course, is an important skill people have acquired over time to support empathy.

The common “satisfaction survey” is a classic example of technology’s limitations in solving even the most basic questions of how someone feels. In principle, it’s a powerful way to gain an understanding of how people feel about a product or service. In practice, it’s clunky, inaccurate, and long overdue for a remake.

Consider the survey prompt found on most store receipts: “Please rate your experience from 1 to 10, and share why.” Compare this with how a person would approach the same challenge: by simply asking “What’d you think of the experience?” and then inferring the “score” from the language used and the overall context. While we don’t need to ask for it explicitly, machines do.

Looking in the mirror

In addition to helping us better understand one another, NLP can also bring a better understanding of ourselves. Language is the most detailed window into our thoughts and feelings. When technology can begin to understand us as we are (not as how it wants us to be), it can become a true partner to help us discover how best to grow and improve.

Take the often-dreaded performance review and the various biases that plague it. When you ask people whether they might be biased at work, even subconsciously, they will vigorously deny it. However, studies of performance reviews show widespread, unconscious bias.

Analysis from my team showed that, when men reviewed other men, they overwhelmingly used passive language (“they could be more proactive”). When these same men reviewed women, though, they often used finger-pointing language (“you should work on your attention to detail”). Using data-driven technology, we were able to provide further insight into this hidden bias that many of us unknowingly carry. Fortunately, AI can put us on the path toward correcting it.

To solve the world’s most challenging “people problems,” whether by developing better products or enabling better understanding and more fairness in the workplace, we need technology to demonstrate empathy. When we leverage advances that combine both heart and mind, we can further develop and deliver the people-centric solutions we all deserve.

Armen Berjikly serves as senior director of strategy at Ultimate Software, where his expertise in human-computer interaction drives Ultimate’s transformative artificial intelligence platform and direction.

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