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

Posted Nov 26
# Tech Talk
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SPEAKER
Alan Cowen
Alan Cowen
Alan Cowen
Chief Scientist and CEO @ Hume AI

Dr. Cowen is an emotion scientist, data scientist, and founder of Hume AI. He was previously a researcher at the University of California, Berkeley and research scientist at Google, where he helped establish affective computing research efforts. His discoveries have been featured in journals such as Nature, PNAS, and Science Advances, and covered in press outlets ranging from CNN to Scientific American. His research applies new statistical and ML tools to address how emotional behaviors can be evoked, conceptualized, predicted, and annotated, how they influence our social interactions, and how they bring meaning to our everyday lives.

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Dr. Cowen is an emotion scientist, data scientist, and founder of Hume AI. He was previously a researcher at the University of California, Berkeley and research scientist at Google, where he helped establish affective computing research efforts. His discoveries have been featured in journals such as Nature, PNAS, and Science Advances, and covered in press outlets ranging from CNN to Scientific American. His research applies new statistical and ML tools to address how emotional behaviors can be evoked, conceptualized, predicted, and annotated, how they influence our social interactions, and how they bring meaning to our everyday lives.

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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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