Signs of depression can be difficult to spot, even for a trained professional. But could you teach an AI to reliably pick up on subtle changes in mood and behavior?
CompanionMX, a Boston-based startup, has created a mental health tool to do exactly that. Its platform uses AI to help users suffering from depression track changes in their emotional state over time.
To do so, co-founder Skyler Place and his team had to overcome one of emotion AI’s biggest hurdles — building an accurate data set for training. Because at the end of the day, if you collect the wrong data or get sloppy with the labeling, the resulting algorithms can quickly cause more harm than good.
The trend of emotion AI has been met with skepticism by many, for exactly that reason. In some cases, these concerns tie back to the limitations of relying on facial recognition, which some experts say results in a much-too-simplistic understanding of human emotions.
Holding Ph.D.s in psychology and cognitive science, Place knows as well as anyone that a simplistic approach won’t do.
To avoid bias and spurious correlations, CompanionMX rooted its analysis in known symptoms of depression. Through the app, the user records a 30-second audio diary commenting on their day, which an algorithm uses to analyze their vocal patterns to determine their energy level. Meanwhile, the app collects data on their texting frequency and phone calls out, as well as distance traveled via GPS — proxies for real-world behaviors like social engagement and physical activity.
Combined, these data points form a well-rounded picture of a patient’s mental state while minimizing the barrier to participation for users, Place said. Here’s what his team learned from translating those data points into a reliable model.