Foreword The twenty-first century has witnessed an unprecedented explosion of data and an equally unprecedented concentration of the means to process it. While the digital revolution promised to democratise knowledge, the reality has been rather different: the infrastructures of artificial intelligence – the cloud servers, the training datasets, the algorithmic architectures – remain overwhelmingly concentrated […]

Foreword

The twenty-first century has witnessed an unprecedented explosion of data and an equally unprecedented concentration of the means to process it. While the digital revolution promised to democratise knowledge, the reality has been rather different: the infrastructures of artificial intelligence – the cloud servers, the training datasets, the algorithmic architectures – remain overwhelmingly concentrated in the hands of a few corporations headquartered in the Global North. For scholars in Africa, Asia, and Latin America, this asymmetry poses a fundamental challenge: how can we conduct rigorous social science research when the very tools we depend upon are designed elsewhere, for other purposes, embedding assumptions and priorities that may not align with our own?

  1. The Crisis of Social Science Research in the Data Age

The contemporary social scientist confronts a paradox. Never before has so much information about human societies been available; never before have the tools for processing that information been so powerful. Yet the lived experience of researchers – particularly those working in under-resourced institutions across the Global South – is often one of frustration, fragmentation, and futility. The gap between what is theoretically possible and what is practically achievable grows wider with each passing year. To understand why this is so, we must examine the structural contradictions that define social science research in the current conjuncture.

1.1 The Bandwidth Paradox: Data Abundance versus Cognitive Limits

The first contradiction arises from the collision between the explosive growth of available data and the biological constraints of human cognition. Consider the situation facing a researcher studying, say, the political economy of extractive industries in West Africa. Relevant materials exist in multiple languages (English, French, Portuguese, Arabic, and numerous African languages); in multiple formats (government reports, corporate filings, news articles, academic papers, social media posts, satellite imagery, audio recordings of community meetings); and across multiple jurisdictions (national, regional, international). The total volume of potentially relevant information far exceeds what any individual, or even any team, could possibly read, let alone analyse.

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