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

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.

The Trust Recovery Journey. The Effect of Timing of Errors on the Willingness to Follow AI Advice.

“…In another between-subjects study with high and low accuracy advice, in which model accuracy could be deduced from comparing the AI advice to the correct answer (ground-truth) after each task, participants trusted and relied significantly more on highly accurate advice [32]. Finally, trust may be affected not only by what participants know about the system but also by what they (subliminally) expect from it: a mismatch in expectations could lead to a decline in trust as Ooge and Verbert found [48].…”

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

“…Interpretable and explainable models diminish the black-box nature of AI, enable understanding of the reasons behind any specific decision, and promote the acceptance of AgriDSS (Dara et al, 2022. Ooge and Verbert (2022) suggest that improved model transparency with tailored explanations and designed visualizations can foster user trust and acceptance of AI.…”