“The project seeks to use social signal processing (SSP), a computational approach that detects subtle cues in behavior that are typically invisible. For example, talk time, interruptions and body movements from health care providers might differ based on a patient’s race, gender or socioeconomic status.” - Nadir Weibel, Design Lab Faculty

Individuals have their own inherent biases. Most are harmless – preferred foods, favorite cars, go-to streaming services. However, biases tied to race, gender, sexual orientation and socioeconomic status have serious consequences.

This is particularly true in medicine. Unintentional, hidden, biases may perpetuate healthcare disparities. While providers are not acting out of malice, these attitudes could have significant impacts on patient care.