This paper examines emerging approaches to cope with the limitation of fairness in the algorithmic management (AM) of food delivery platforms. We refer to the relatively new concept of "fairness engineering" 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 backdrop of the upcoming AI Act of the EU, we first review the legal environment governing high-risk AI in the workplace, focusing on its links to the Charter of Fundamental Rights of the European Union. We then illustrate 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 income distribution.

Although AM can streamline operations, it raises critical questions about transparency, power imbalances, information asymmetry, and non-discrimination. Drawing on four recent studies, we explore a spectrum of fairness interventions, from regulatory policies that cap platform commissions to optimization models that redistribute tasks more equitably.

Comparative analysis reveals that fairness in AM can be defined and operationalized differently, whether through economic equity among stakeholders, proportional income distribution, or balanced workloads. We also find considerable variation regarding the attempts to deal with fairness issues. "Fairness engineering" occurs at different levels and stages of the AM implementation and application process.

We highlight the need for transparent algorithmic systems, participatory governance structures, and more rigorous fairness metrics. In addition, future research should address intersectional biases in performance data and evaluate the long-term impacts of fairness interventions, ensuring that AM practices align with fundamental rights and worker well-being. The comparison calls for a systematic overview and evaluation of existing approaches.