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In supervised learning, low quality annotations lead to poorly performingclassification and detection models, while also rendering evaluationunreliable. This is particularly apparent on temporal data, where annotationquality is affected by multiple factors. For example, in the post-hocself-reporting of daily activities, cognitive biases are one of the most commoningredients. In particular, reporting the start and duration of an activityafter its finalisation may incorporate biases introduced by personal timeperceptions, as well as the imprecision and lack of granularity due to timerounding. Here we propose a method to model human biases on temporalannotations and argue for the use of soft labels. Experimental results insynthetic data show that soft labels provide a better approximation of theground truth for several metrics. We showcase the method on a real dataset ofdaily activities.

When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations