Each record is labelled according to its actual stage. Drafts are not presented as peer-reviewed publications; public links are added only when a text has a verifiable external record.
Examines how AI systems reshape virtual and narrative identity through algorithmic co-authorship, with particular attention to authenticity, epistemic legitimacy, and the right to retain authorship over one’s self-narration.
Develops a taxonomy of epistemic failure in large language models, distinguishing confident error, synthetic coherence, uncertainty compression, and context-sensitive reliability collapse.
Explores the gap between linguistic fluency and semantic grounding by examining how symbolic structures are compressed in representation learning.
Argues that dignity-preserving design requires more than fairness metrics and must include contestability, recognition, meaningful human review, and access to remedy.
Proposes design principles for embedding ethical constraints across models, interfaces, institutional roles, and remediation processes.
Investigates whether AI-generated outputs can function as testimony and what forms of human, institutional, and technical warrant must accompany them.
Revisits the distinction between rule-governed symbol manipulation and understanding in the context of contemporary language models.
Defends democratic rather than merely technocratic governance of AI systems, with attention to public reason, responsibility, institutional contestability, and legitimacy.
A design-oriented ethics manuscript on protective user experiences, safety guardrails, and the normative role of interaction design.