Suspicious Behavior
Linda Kronman, Andreas Zingerle · 2022
Suspicious Behavior is a fictional annotation tutorial inviting readers to critically examine machine learning datasets assembled for anomaly detection in surveillance footage (Figure 1). Mimicking existing annotation interfaces [11] and practices [12] the tutorial, although fictive, provides insight into the hidden work of crowdsourced labor and how annotators engage in decision making (Figure 2). Readers in the role of annotator-trainees, advance through an introduction and three ‘advanced modules’ of the tutorial performing what is called ‘Human Intelligence Tasks.’ The assignment is to spot suspicious behavior in video segments. Throughout the interactive story the reader gets trained for an optimized annotation workflow, balancing between accuracy and efficiency.