A recent report titled "Food-Checker: A mobile-based crowdsourcing application for dual quality of food" explores the potential of a mobile app in addressing the issue of dual quality food products across different Member States. The report, conducted in response to the European Parliament's emphasis on establishing a permanent monitoring system, investigates the effectiveness of the Food-Checker app in identifying and tracking products sold on the single market. Using artificial intelligence technology, the app was tested in five Member States, including Germany, France, Italy, Poland, and Romania, over a six-month period.
Title: Food-Checker: A mobile-based crowdsourcing application for dual quality of food
URL: https://doi.org/10.2760/242232
Year: 2024
Authors: Di Marcantonio, F; Nedelcu, BR; Padiu, B; Rebedea, T; Barreiro-Hurle, J; Ciaian, P
Journal: Publications Office of the European Union
Abstract: In the context of the policy debate and initiatives to address misleading practices that suggest to consumers that products marketed under the same brand and in the same or similar packaging have the same composition or characteristics across different Member States when this is not the case (often referred to as ‘dual quality’ (DQ)), the European Parliament has emphasised the importance of establishing a permanent monitoring system to track products sold on the single market. This feasibility study aims to explore the effectiveness of a crowdsourcing mobile app (Food-Checker) as a tool to monitor the occurrence of DQ. The app uses artificial intelligence (AI) technology and was tested using an awareness campaign and incentives to motivate users to submit product images. The app was piloted in five Member States (Germany, France, Italy, Poland and Romania) between September 2022 and March 2023. The study results demonstrate that Food-Checker can effectively utilise AI technology for monitoring DQ, particularly for capturing and extracting information from product packaging. Although there was a substantial awareness campaign, users’ engagement with the app was limited. In this regard, monetary rewards were found to be more effective than behavioural interventions. As a result of low user engagement and a small sample size, AI could not be fully trained in certain functions relevant to identifying DQ − such as identifying the same or similar branded products, distinguishing different product versions within a Member State and detecting DQ practices. The app could be a viable solution for monitoring DQ if the AI behind the app is further trained to enhance its performance and effective engagement tools are developed.
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