A Hegelian Reconstruction of the Immanent Contradiction of Predictive Rationality in Artificial Intelligence

Document Type : Research Paper

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shiraz uni

Abstract

This article asks whether the predictive accuracy of artificial intelligence systems can be regarded as a form of understanding the future, and what happens to the epistemic relation between the model and its object when prediction becomes institutionally operationalized. The central claim is that the limitation of these systems is not merely statistical error or lack of causal explanation. Institutionalized prediction may alter the conditions of realization and future data, and then reinterpret the outcome of its own intervention as a confirmation of its initial necessity. The research employs conceptual-dialectical reconstruction and immanent critique to link Hegelian concepts such as understanding, determinate negation, real possibility, actuality, and necessity with Catherine Malabou's notion of plasticity and the contemporary literature on performative prediction. Predictive policing is utilized as a conceptual case study to test the argument. First, "algorithmic understanding" is the legitimate operation of stabilizing variables and generalizing conditional relations. Second, "predictive rationality" emerges when probabilistic output becomes the dominant criterion for allocating opportunities and interventions. Third, the feedback loop generates an immanent contradiction: the model requires relative stability of conditions for prediction, but its institutional application transforms those very conditions.

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Journal of Philosophical Theological Research

Articles in Press, Accepted Manuscript
Available Online from 07 October 2026

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