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The growing utilization of machine learning (ML) in decision-making processesraises questions about its benefits to society. In this study, we identify andanalyze three axes of heterogeneity that significantly influence the trajectoryof ML products. These axes are i) values, culture and regulations, ii) datacomposition, and iii) resource and infrastructure capacity. We demonstrate howthese axes are interdependent and mutually influence one another, emphasizingthe need to consider and address them jointly. Unfortunately, the currentresearch landscape falls short in this regard, often failing to adopt aholistic approach. We examine the prevalent practices and methodologies thatskew these axes in favor of a selected few, resulting in power concentration,homogenized control, and increased dependency. We discuss how this fragmentedstudy of the three axes poses a significant challenge, leading to animpractical solution space that lacks reflection of real-world scenarios.Addressing these issues is crucial to ensure a more comprehensive understandingof the interconnected nature of society and to foster the democratic andinclusive development of ML systems that are more aligned with real-worldcomplexities and its diverse requirements.

Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements