Algorithmic Datafication, Dominant Epistemic Frameworks, and Hermeneutical Injustice in Artificial Intelligence
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Algorithmic Datafication, Dominant Epistemic Frameworks, and Hermeneutical Injustice in Artificial Intelligence
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Conceptual Foundations of Datafication and Epistemic Power in Artificial Intelligence
The rapid growth of artificial intelligence has introduced a new era of datafication, the highly intensive process according to which social life turns into measurable, machine-readable elements. Although it is glorified as an analytical instrument and predictive tool, such a transformation is not epistemically neutral. Datafication entails systematic suppositions regarding the character of knowledge, that which is worth quantifying, and the nature of intelligibility of understanding, which is assessable under computational systems. They rarely undergo questioning but are instead operationalised in terms of the data infrastructures, training pipelines, and model architectures that encode complex social matters into determined classes. By so doing, AI systems end up embodying prevailing epistemic apparatus in favour of worldviews that conform to institutional, Western, technocratic, or majoritarian views.
Marginalisation of Alternative Epistemologies Through Algorithmic Representation
These implications are severe for marginalised communities. Their ways of knowing are often contextual, relational, embodied, or historically situated, yet they are difficult to compress into the reductive variables that algorithmic systems demand. When such epistemologies are absent or misrepresented within datasets and modelling logics, AI systems become incapable of meaningfully representing the experiences or interpretive resources of these communities. This absence is not merely a technical oversight but constitutes a form of hermeneutical injustice, whereby marginalised groups are denied adequate interpretive resources for making sense of their experiences within dominant systems of meaning.
Algorithmic Construction of Hermeneutical Injustice Through Datafication
Hermeneutical injustice becomes algorithmically embedded through the process of datafication. Classification systems, labelling frameworks, and model outputs depend upon predefined interpretive categories that themselves emerge from historically situated relations of power. Once incorporated into artificial intelligence systems operating across healthcare, education, employment, policing, and other domains, these categories increasingly function as authoritative representations of social reality. Individuals whose experiences fall outside these predefined classifications are consequently misinterpreted, excluded, or rendered invisible. Artificial intelligence therefore assumes the role of an epistemic gatekeeper by determining which forms of knowledge become intelligible within computational systems and which remain excluded from recognition.
Promoting Epistemic Pluralism Through Responsible Artificial Intelligence Design
Recognising artificial intelligence as a mechanism through which datafication reproduces hermeneutical injustice highlights the urgent need to reconsider how knowledge is encoded into computational systems. Addressing these challenges requires methodological pluralism, participatory system design, and sustained epistemic reflexivity that questions the dominance of narrow interpretive models while creating space for diverse forms of knowledge production and meaning-making. Without such interventions, artificial intelligence will continue reinforcing existing epistemic hierarchies by institutionalising exclusions at the level of interpretation and reproducing structural inequalities through seemingly objective computational processes.