Abstract
Several industries and sectors such as health care, agriculture, and finance exploit the added value of data to produce valuable insights for decision-making. The case of so-called ’boar taint’, the unwanted taste and odor that can be present in meat of entire male pigs, is one real-life scenario that showcases the added value of utilizing collected data. This information may yield insights for pig farmers about how they could adjust their management to reduce boar taint. This study examines multiple predictive data-driven approaches coupled with eXplainable AI (XAI) methods, evaluating them against various explainable metrics while trying to generate actionable insights and recommendations. Specifically, in this approach, the examined use case was modeled as a binary classification task resulting in a highly imbalanced dataset. This yielded some functional attributes regarding the farm/stable and slaughterhouse conditions, such as the type of feed, type of ventilation system, pharmaceutical treatment, floor type, and the duration of waiting in lairage.
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