This paper examines emerging approaches to cope with the limitation of fairness in the algorithmicmanagement (AM) of food delivery platforms. We refer to the relatively new concept of "fairnessengineering" to focus on attempts to realize fair outcomes of AM at different stages of the development,implementation, and application process at the workplace of food delivery drivers. Against the backdropof the upcoming AI Act of the EU, we first review the legal environment governing high-risk AI in theworkplace, focusing on its links to the Charter of Fundamental Rights of the European Union. We thenillustrate how food delivery, a fast-growing sector of platform work, relies on automated decision-making systems that influence task assignments, performance monitoring, work schedules, and incomedistribution. Although AM can streamline operations, it raises critical questions about transparency,power imbalances, information asymmetry, and non-discrimination. Drawing on four recent studies, weexplore a spectrum of fairness interventions, from regulatory policies that cap platform commissions tooptimization models that redistribute tasks more equitably. Comparative analysis reveals that fairness inAM can be defined and operationalized differently, whether through economic equity among stakeholders,proportional income distribution, or balanced workloads. We also find considerable variation regarding theattempts to deal with fairness issues. “Fairness engineering” occurs at different levels and stages of theAM implementation and application process. We highlight the need for transparent algorithmic systems,participatory governance structures, and more rigorous fairness metrics. In addition, future researchshould address intersectional biases in performance data and evaluate the long-term impacts of fairnessinterventions, ensuring that AM practices align with fundamental rights and worker well-being. Thecomparison calls for a systematic overview and evaluation of existing approaches.