What machines mean,
what they risk,
what we owe.
Independent philosophical research on the epistemic foundations, ethical architecture, and social implications of artificial intelligence.
Artur Ziganshin · Master of Philosophy · PhD of Philosophy
Research Areas
Epistemic Risks
How do AI systems generate persuasive but weakly grounded claims? Drawing on process reliabilism and virtue epistemology, I develop frameworks for auditing epistemic reliability before deployment.
Explore →Ethical Architecture
Design principles for embedding normative constraints at model, interface, and institutional levels — so ethics is foundational, not an afterthought.
Explore →Human Dignity
A framework grounded in Kantian ethics and capabilities theory for preserving agency, respect, and contestability in AI-mediated decisions.
Explore →Recent Papers
View allEpistemic Risk Surfaces in Large Language Models
This paper develops a granular taxonomy of epistemic failure in large language models, distinguishing between confident error, synthetic coherence, and context-sensitive reliability collapse. I argue that benchmark performance cannot substitute for process-level justification and propose an audit architecture grounded in process reliabilism and virtue epistemology.
Linguistic Symbolism and Meaning Compression in Machine Learning
By analyzing how symbolic structures are compressed during representation learning, this preprint examines the gap between linguistic fluency and semantic grounding. I show why lexical competence in model outputs can mask referential fragility and propose criteria for distinguishing symbolic simulation from meaningful reference.
Human Dignity Constraints for Autonomous Decision Systems
This paper argues that dignity-preserving design requires more than fairness metrics. Drawing on Kantian ethics and capabilities theory, I outline institutional and interface-level constraints that preserve contestability, recognition, and agency in automated welfare, labor, and healthcare decisions.
Latest Writing
View allThe Epistemic Mirror
Weekly philosophical analysis of AI developments. No hype, no jargon — just clarity.
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I investigate the philosophical foundations of artificial intelligence — focusing on what AI systems know, how they fail, and what we owe to the people affected by their decisions.
My work sits at the intersection of epistemology, ethics, and philosophy of language. I publish research on PhilArchive and write weekly philosophical analysis for a growing community of readers.
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