How To Build Technology With Empathy: Addressing the Need for Psychologically Valid Data

Name: How%20To%20Build%20Technology%20With%20Empathy:%20Addressing%20the%20Need%20for%20Psychologically%20Valid%20Data
Uploaded: 2021-11-26T06:10:25.324Z

Posted Nov 26, 2021 | Views 2.9K

# Tech Talk

Alan Cowen

Chief Scientist and CEO @ Hume AI

Alan Cowen, PhD is an applied mathematician and computational emotion scientist who has developed new data-driven methods to study human experience and expression. He is the CEO of Hume AI, a startup that provides next-generation tools to understand expressive behavior in audio, video, text, and images. Prior to founding Hume AI, he was a researcher at Google AI, where he helped establish affective computing research efforts. Cowen’s studies in Nature, PNAS, Science Advances, and Nature Human Behavior are the first to analyze facial and vocal expressions at a sufficient scale and level of precision to draw inferences from their occurrence in everyday life across cultures. His work has given rise to a mathematically-based theory of emotion, semantic space theory, which conceives of emotions as dimensions of a complex, multidimensional space.

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SUMMARY

Building emotional awareness into AI is a central goal for technology companies standing to impact billions of potential users. From digital assistants that can sense and soothe your frustrations to photo apps that can identify a warm smile, empathic AI will be central to a future in which our everyday interactions with technology ultimately serve our emotional well-being. But most companies are missing the right training data to make this human-centric future for empathic AI a reality. Large-scale, globally diverse data with a multitude of emotional expressions and contexts is essential to paint a scientifically valid picture of human emotion. I will discuss three principles for gathering this kind of rich, globally diverse, psychologically valid emotion data at scale, and explain how we implemented them at Hume AI to gather 3 million self-report and perceptual judgments of 1.5 million human emotional behaviors. Using Hume’s models as an example, I will showcase how ML models trained on this data can infer human emotional behavior with more accuracy and nuance than was previously possible. Finally, I will summarize ethical guidelines for deploying these powerful new empathic technologies at scale.

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