DATA JUSTICE IN FOOD MARKETING: WILL ALGORITHMS ELIMINATE DATA BIAS ON PLATFORMS?

Abstract

The increasing reliance on algorithmic systems in food marketing creates both opportunities and risks for equity and accountability. This study is motivated by concerns that algorithms, while promising personalisation and efficiency, may also reproduce biases and obscure power relations. The objective is to examine whether and how existing research addresses three facets (nutrition disclosure, misinformation, and big data/AI) and to assess the relationship between data justice, algorithmic decision-making, and marketing practices. Methods include a systematic literature review of studies published between 2013 and 2023, resulting in twelve works meeting defined criteria, combined with a one-month monitoring of 3,300 social media posts across major platforms. Findings indicate that algorithms enable more targeted and personalised marketing yet frequently amplify biases due to flawed inputs and limited training diversity. Only a small share of studies integrate all facets, while platform monitoring shows substantial reach and enduring visibility via news/blogs, with topical salience around “food,” “nutrition,” “data,” and “precision nutrition.” The results suggest algorithmic “nutrition labels,” diverse datasets, and transparent governance mechanisms are essential innovations to align data-driven marketing with principles of justice and public interest.