Unraveling the Interconnected Axes of Heterogeneity in Machine Learning
  for Democratic and Inclusive Advancements
Maryam Molamohammadi Afaf Taik Nicolas Le Roux Golnoosh Farnadi
The growing utilization of machine learning (ML) in decision-making processes
raises questions about its benefits to society. In this study, we identify and
analyze three axes of heterogeneity that significantly influence the trajectory
of ML products. These axes are i) values, culture and regulations, ii) data
composition, and iii) resource and infrastructure capacity. We demonstrate how
these axes are interdependent and mutually influence one another, emphasizing
the need to consider and address them jointly. Unfortunately, the current
research landscape falls short in this regard, often failing to adopt a
holistic approach. We examine the prevalent practices and methodologies that
skew these axes in favor of a selected few, resulting in power concentration,
homogenized control, and increased dependency. We discuss how this fragmented
study of the three axes poses a significant challenge, leading to an
impractical solution space that lacks reflection of real-world scenarios.
Addressing these issues is crucial to ensure a more comprehensive understanding
of the interconnected nature of society and to foster the democratic and
inclusive development of ML systems that are more aligned with real-world
complexities and its diverse requirements.