Algorithmic Rationality And Cultural Meaning: A Philosophical Inquiry Into AI-Driven Decision-Making
Published 2025-11-10
Keywords
- Algorithmic rationality, Cultural axiology, AI ethics, Interpretable decision systems, Computational epistemology, Intercultural judgment.

This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
The decision systems based on algorithms are getting more and more situated as the objective engines of reason, but the concept of rationality that is inherent in AI is neither cross-culturally neutral nor culturally neutral. The paper challenges the philosophical principles of AI-based decision-making through the lens of the conflict between algorithmic rationality (formal logic, optimization, probabilistic inference) and cultural meaning systems used to define the decision interpretation, legitimization, and experience. The query contends that AI is not just calculating options but stipulates a collection of epistemic presumptions regarding efficiency, consistency, prediction, and utility and is antagonistic to plural cultural organizations that endorse relationality, moral inheritance, ritual rationality, intergenerational obligations, and contextual judgment. This study integrates theoretical discussions in computational rationalism, hermeneutics, moral philosophy, and cultural axiology by relying on a conceptual, secondary research methodology that is based on cross-cultural philosophy, STS (Science and Technology Studies) and critical AI ethics. The article suggests a two-level analytical system in which AI rationality is evaluated as a formal infrastructure of thinking and cultural meaning is evaluated as an interpretive beneath layer affecting trust, acceptance, contestation and shared sense-making. It is projected that contributions will be made through: (1) the re-theorizing of AI rationality beyond instrumental logic; (2) normative blind spots in culturally mediated situations of such decisions; and (3) the provision of philosophical underpinnings to culturally congruent AI governance. The research can be applicable to scholars and practitioners who are trying to match AI systems with ethically plural and culturally relevant decision paradigms.