Best Paper
There has been a great deal of scholarly attention on issues of identity-related bias in machine learning. Much of this attention has focused on data and data workers, workers who do annotation tasks. Yet tech workers—like engineers, data scientists, and researchers—introduce their own “biases” when defining “identity” concepts. More specifically, they instill their own positionalities, the way they understand and are shaped by the world around them. Through interviews with industry tech workers who focus on computer vision, we show how workers embed their own positional perspectives into products and how positional gaps can lead to unforeseen and undesirable outcomes. We discuss how worker positionality is mutually shaped by the contexts in which they are embedded. We provide implications for researchers and practitioners to engage with the positionalities of tech workers, as well as those in contexts outside of development that influence tech workers.
doi.org/10.1145/3613904.3641890
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)