Techno/Ecofeminism in Action: Fair and Responsible Resource Allocation for Sustainable Data Science Pipelines

This paper introduces a dispatching approach to allocating computing resources for executing various activities within data science pipelines. The allocation strategy incorporates quantitative metrics-such as workload, performance in time, and memory consumption-and qualitative metrics emphasising fairness, responsibility, and sustainability. These qualitative considerations include the geographic location of servers, their CO2 footprint, the frugality of data processing and analytics models, the conditions under which the data are produced, and the expected collective benefit of the processing outcomes. By integrating these qualitative metrics into resource-dispatching strategies and decision-making processes, the proposed algorithm aims to transform the execution of data science pipelines into a more ethical and equitable practice. This approach aligns with the principles of techno- and ecofeminism, advocating for technological solutions that prioritize collective social and environmental progress over purely capitalist gains. In this context, techno/ecofeminism provides a critical lens, emphasizing the importance of inclusivity, sustainability, and shared benefits in developing and deploying data-driven technologies. This work challenges extractive and inequitable models by grounding the dispatching strategy in these principles, proposing an alternative framework that leverages technology for holistic and equitable advancement.