Challenging Bias in Data and Tools

Recognizing the bias in data and tools is crucial, as it stems from the lack of human diversity in the individuals creating and testing these technologies. Merely diversifying the workforce may not always solve the underlying issue, especially when biases could be related to factors like lighting conditions. It's essential to address biases in data sets and tools by comprehensively understanding the root cause rather than just focusing on diversity hiring.