research-article

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

Authors:

Maryam Molamohammadi,

Nicolas Le Roux,

Golnoosh FarnadiAuthors Info & Claims

EAAMO '23: Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization

Article No.: 33, Pages 1 - 12

https://doi.org/10.1145/3617694.3623256

Published: 30 October 2023 Publication History

Abstract

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.

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Cited By

Farnadi GHavaei MRostamzadeh NSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692594(13072-13085)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692594
Herbert NDorodchi MZhange MCooper S(2024)Breaking Barriers: Overcoming Resistance to Curriculum IndigenisationProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690104(53-59)Online publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1145/3649165.3690104

Index Terms

Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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EAAMO '23: Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization

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DOI:10.1145/3617694

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Farnadi GHavaei MRostamzadeh NSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692594(13072-13085)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692594
Herbert NDorodchi MZhange MCooper S(2024)Breaking Barriers: Overcoming Resistance to Curriculum IndigenisationProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690104(53-59)Online publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1145/3649165.3690104

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