Emotional awareness is intuitive to us. We are wired to know when we and others are feeling angry, sad, disgusted… because our survival depends on it.
In other words, the decoding of the contextual nuances of these emotional expressions has served us since time immemorial.
Presumably, artificial intelligence exists to serve us. So, to build truly ‘intelligent’ AI that adequately serves humanity, the ability to detect and understand human emotion ought to take center-stage, right?
Turns out, it’s not that simple.
Inside ≠ Out
Microsoft and Apple’s mistake is two-pronged. First, there was an assumption that emotions come in defined categories: Happy, Sad, Angry, etc. Second, that these defined categories have equally defined external manifestations on your face.
To be fair to the tech behemoths, this style of thinking is not unheard of in psychology. Psychologist Paul Ekman championed these ‘universal basic emotions’. But we’ve come a long way since then.
In the words of psychologist Lisa Feldman Barrett, detecting a scowl is not the same as detecting anger. Her approach to emotion falls under psychological constructivism, which basically means that emotions are simply culturally specific ‘flavors’ that we give to physiological experiences.
Your expression of joy may be how I express grief, depending on the context. My neutral facial expression may be how you express sadness, depending on the context.
So, knowing that facial expressions are not universal, it’s easy to see why emotion-recognition AI was doomed to fail.
Much of the debate around emotion-recognition AI revolves around basic emotions. Sad. Surprised. Disgusted. Fair enough.
But what about the more nuanced ones… the all-too-human, self-conscious emotions like guilt, shame, pride, embarrassment, jealousy?
A substantive assessment of facial expressions cannot exclude these crucial experiences. But these emotional experiences can be so subtle, and so private, that they do not produce a consistent facial manifestation.
What’s more, studies on emotion-recognition AI tend to use very exaggerated “faces” as origin examples to feed into machine-learning algorithms. This is done to “fingerprint” the emotion as strongly as possible for future detection.
But while it’s possible to find an exaggeratedly disgusted face, what does an exaggeratedly jealous face look like?
An Architectural Problem
If tech companies want to figure out emotion-recognition, the current way AI is set up probably won’t cut it.