In a response to Dr Ozan Aksoy and colleagues' proposal for three key principles to advance disciplinary standards in sociology, Dr Fanqi Zeng argues for a broader perspective.
While Dr Zeng agrees with the three proposed principles – the integration of theory and empirics, adopting open science practices, and cautious engagement with wider societal debates – he suggests that they are framed largely from the perspective of European and North American sociology.
Two developments, the changing geography of sociological knowledge production and the disciplinary reach of artificial intelligence (AI), make a wider frame necessary.
First, the article argues that sociological knowledge continues to be produced predominantly in the Global North, with theories and datasets generated there often treated as universally applicable. Large parts of the Global South remain comparatively under-studied and under-resourced, presenting a significant deficit in sociological coverage.
Secondly, the article emphasises that the rapid developments in AI are not just a European challenge, but a global one. At the same time, they are also an opportunity – as routine analytic tasks become more easily automated, researchers have more freedom to dedicate time to tasks which AI cannot complete, such as theory building, contextual interpretation, and identifying the mechanisms that link conditions to outcomes.
Combining these two points presents another opportunity: many societal issues remain understudied, not because they are unimportant but because research capacity is limited and data infrastructure is thin across much of the world. Cheaper computation lowers one barrier to working on this frontier.
Dr Zeng therefore proposes a reorientation of the original statement by Aksoy et al, advocating for deeper and more reciprocal collaboration with researchers in the Global South. Such collaboration should be driven by questions that matter locally, including those motivated by intellectual curiosity as well as policy relevance.
This would achieve three goals. First, it would help sociology respond to the challenges posed by AI by opening areas of inquiry that predictive tools cannot exhaust, since explanation, measurement, and theory-building in under-studied contexts remain fundamentally human tasks.
Second, it would begin to address existing empirical and theoretical gaps by extending and testing sociological theories across a much wider range of cases.
Third, it would improve knowledge diffusion and help narrow methodological inequalities in the use of AI for social research, ensuring that new methods spread across the discipline rather than increasing the divide between core and periphery.
The article concludes:
In sum, the geography of the discipline and the arrival of AI are usually discussed apart. Taken together, they point to a single response.
A sociology that extends its standards to a wider world, and that does so through reciprocal collaboration rather than the export of Northern templates, is better placed to absorb the AI shock, to fill its own empirical deficit, and to remain credible when it speaks on matters of public importance.
