Visually Explaining Uncertain Price Predictions in Agrifood: A User-Centred Case-Study

Jeroen Ooge and Katrien Verbert
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Jeroen Ooge: Department of Computer Science, KU Leuven, 3001 Leuven, Belgium
Katrien Verbert: Department of Computer Science, KU Leuven, 3001 Leuven, Belgium

Agriculture, 2022, vol. 12, issue 7, 1-25

Abstract: The rise of ‘big data’ in agrifood has increased the need for decision support systems that harvest the power of artificial intelligence. While many such systems have been proposed, their uptake is limited, for example because they often lack uncertainty representations and are rarely designed in a user-centred way. We present a prototypical visual decision support system that incorporates price prediction, uncertainty, and visual analytics techniques. We evaluated our prototype with 10 participants who are active in different parts of agrifood. Through semi-structured interviews and questionnaires, we collected quantitative and qualitative data about four metrics: usability, usefulness and needs, model understanding, and trust. Our results reveal that the first three metrics can directly and indirectly affect appropriate trust, and that perception differences exist between people with diverging experience levels in predictive modelling. Overall, this suggests that user-centred approaches are key for increasing uptake of visual decision support systems in agrifood.

Keywords: visual analytics; visualisation; uncertainty; explainable artificial intelligence; decision support systems; mixed-methods; thematic analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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