Investigating Labeler Bias in Face Annotation for Machine Learning
This addresses ethical issues in AI by highlighting how labeler bias affects datasets for applications like healthcare and law enforcement, though it is incremental as it focuses on measuring an under-explored challenge.
The study investigated labeler bias in face annotation for machine learning, finding that participants' stereotypes and demographics influence labeling decisions, which can lead to biased datasets and unfair AI outcomes.
In a world increasingly reliant on artificial intelligence, it is more important than ever to consider the ethical implications of artificial intelligence on humanity. One key under-explored challenge is labeler bias, which can create inherently biased datasets for training and subsequently lead to inaccurate or unfair decisions in healthcare, employment, education, and law enforcement. Hence, we conducted a study to investigate and measure the existence of labeler bias using images of people from different ethnicities and sexes in a labeling task. Our results show that participants possess stereotypes that influence their decision-making process and that labeler demographics impact assigned labels. We also discuss how labeler bias influences datasets and, subsequently, the models trained on them. Overall, a high degree of transparency must be maintained throughout the entire artificial intelligence training process to identify and correct biases in the data as early as possible.