Privacy Curtain—Sacrificing Causal Inference in Predictive Systems? Revisiting Value Conflict from a Modern Paradox

In the era of machine learning-driven predictive systems, data anonymization and perturbation techniques have become widespread for protecting individual and group privacy and simulating rare events. Yet these approaches frequently undermine causal inference, reduce model accuracy, and enlarge predictive gaps—the critical disparity between statistical inferences derived from training data and definitive operational predictions.

This review investigates the inverse relationship between privacy preservation and predictive performance, conceptualizing it as a contemporary value conflict embedded in technicism: over-reliance on technical fixes that unintentionally intensify systematic biases within intelligent automation.

Central research questions include the following: Does privacy protection via data perturbation obstruct models’ direct grasp of human behavioral patterns? What are the downstream effects of predictive gaps on task automation and societal biases? Drawing heavily on Max Weber’s value pluralism, instrumental rationality, and the (iron cage) metaphor—alongside Isaiah Berlin’s insights—the analysis demonstrates how technocratic solutions and unilateral privacy advocacy fail to resolve the inherent tension, reducing a deep value clash to mere technical problems and perpetuating a vicious cycle of increasing complexity.

A hybrid methodology combined horizontal and vertical literature reviews. From Scopus, IEEE, and Google Scholar (2019–2025), 21 sources were selected using keywords “Machine Learning,” “Model Accuracy,” and “Privacy,” then coded in MAXQDA 24 across five categories, revealing four major gaps: insufficient social-science framing, the need for multidimensional assessment, technical–social disconnect, and unexamined sociological privacy–accuracy links.

Findings show that “infrastructural privacy” widens predictive gaps, echoing Weber’s iron cage where rationality traps rather than liberates. Real-world cases (supermarket pregnancy targeting, counterterrorism metadata analysis) highlight privacy violations and error amplification via proxy data. While protective, synthetic data and differential privacy distort genuine causal relations, fostering biases in sensitive automation domains such as policy-making.

The review concludes that meaningful resolution demands explicit value-based trade-offs_neither absolute technicism nor uncompromising privacy absolutism_to curb systematic biases in socio-technical systems.