Ethical Practices for Collecting Ground-Truth Food Datasets: A Systematic Review

Food image dataset collection for artificial intelligence (AI) studies has ethical concerns that are underestimated. Thousands and millions of images are collected from different sources on the Web, with ethical challenges relating to copyright. While it is difficult to collect large amounts of food images based on permissions, a small number of datasets can affect the performance of the machine learning model (MLM). It becomes imperative to consider a balance between ethics, data collection, and machine learning model performance. We present state-of-the-art ethical considerations for collecting food image datasets. A total of 102 papers were reviewed, and we found that only a few papers (4) reported on the ethical practices they adopted for collecting food image datasets. These ethical practices include obtaining permissions from data sources such as websites and social media sites and obtaining ethical approval for collecting food datasets from participants in food logging studies. For future work, we present opportunities, challenges, and recommendations for considering dataset collection in the food domain. Though there are challenges around collecting datasets that are sufficient for training MLMs, we provide recommendations to balance the trade-off between gathering large datasets ethically and improving the accuracy of MLMs.