In our continuing series on Race in Tech, we explore the problem of bias embedded in the technology itself. Our guests Nayeli Hernandez, Data Analyst for CarDon, Sudha Gayathri, a Clinical Researcher, and Ashish Khandelwal, a postdoc Research Associate and Instructor at the University of Illinois, each bring a unique perspective as it relates to how human biases end up codified in AI models, how this impacts the accuracy of the data as well as the outcomes derived from it. In this episode the panel of guests discuss each area bias can be injected into the process as well has how to resolve the issue using diverse teams and proper mechanisms as well as creating a culture of open discussion and communication.
Discussed in this episode:
AI Engineers Need to Think Beyond Engineering (HBR article)