An independent research platform examining narrative identity, machine testimony, dignity, contestability, and the institutional distribution of responsibility in AI systems.
How algorithmic systems participate in the stories through which persons become intelligible to themselves and others.
Most AI governance begins after a system has already been framed as useful, objective, or inevitable. This programme begins earlier: with the claims, concepts, and power relations that make a system appear entitled to act.
AI systems increasingly produce consequential accounts of character, risk, competence, credibility, and identity. The philosophical problem is not only whether these accounts are accurate, but whether the subject can remain an author rather than an object of description.
Fluency can create the social appearance of knowledge before the conditions of knowledge are present.
Distributed systems often distribute control while concentrating consequences on the affected person.
How AI-mediated systems alter self-understanding, life narratives, authenticity, and the distribution of authorship between people and computational systems.
↗How people and institutions should rely on AI outputs, allocate warranted trust, preserve uncertainty, and recognise reliability collapse.
↗Ethical constraints for systems that classify, rank, recommend, or decide in settings that affect rights, access, recognition, and opportunity.
↗Research grounded in practical experience with digital systems, information harm, online identity, privacy, reputation, and the difficulty of locating responsibility.
↗A wider philosophical programme on relational ontology, freedom as secure belonging, institutions, affect, and resistance to reductive social fusion.
↗A developing programme on responsibility in knowledge practices, scientific ethics, noosphere traditions, and the normative consequences of collective intellectual systems.
↗The paid workspace does not produce a generic ethics score. It creates a versioned argument: what the system claims, where evidence is missing, who remains responsible, what people are owed, and which risks cannot be designed away.
Tests what the system claims, what evidence supports it, and where confidence exceeds warrant.
Locates control, oversight, explanation duties, and the capacity to repair harm.
Examines whether affected people can understand, challenge, refuse, and obtain remedy.
Assesses how profiling and algorithmic interpretation alter a person’s authorship of self.
System assessment, responsibility architecture, executive workshops, and independent review of consequential AI deployments.
Discuss an institutional audit ↗