Diffusion of Authorship

In The You On AI Field Guide

You On AI describes this diffusion honestly. 'Neither of us owns that insight,' the author writes of a moment of productive collaboration with Claude. 'The collaboration does.' The honesty is admirable, and the description of the experience is accurate. But the formulation conceals a question Berger's framework insists on asking: if neither the human nor the machine owns the insight, who is responsible for it? If the insight is wrong — if the reference is misused, if the historical claim is inaccurate, if the recommendation causes harm — the diffusion of authorship becomes a diffusion of accountability. The collaboration owns the insight, but the collaboration is not a person and cannot be held responsible. The human can be held responsible, but did not produce the output alone. The machine cannot be held responsible, because it is not a moral agent.

Berger spent his career insisting that every image carried a politics, and that part of what the politics consisted in was accountability. When you looked at an oil painting, you could ask: who made this, under what conditions, in whose interest, for whom? The questions were answerable because the painting had a specific history. The AI artifact resists these questions not because the history is unknown but because it is distributed across so many actors that no specific history applies. The training corpus is not a person you can locate. The engineers who built the model are many and anonymous. The corporation is a legal fiction designed to diffuse liability. The user wrote the prompt but did not produce the output.

This is not a technicality. It is a political outcome. Systems that produce consequential outputs — medical recommendations, legal briefs, financial analyses, news-adjacent content — need accountability structures, and accountability structures need to locate responsibility somewhere. The diffusion of authorship in AI collaboration tends, in practice, to push responsibility either onto the user (who signs off on an output she did not fully understand) or into nowhere (where responsibility dissolves into the collective agency of the 'system').

The handmade artifact says: I was made, and the marks of my making tell you something about who made me and how and why. The AI-generated artifact says: I appeared, and my surface tells you nothing about the conditions of my production, because my surface is all there is. The first arrangement supports accountability because it supports identification. The second undermines accountability because it undermines identification. This is not a problem new technologies will solve. It is a feature of the technology's architecture, and addressing it requires institutional and legal structures that do not yet exist.

Origin

The concept is developed in Chapter 3 of this volume, extending Berger's analysis of authorship in Ways of Seeing and in his later writing on collaborative work with photographer Jean Mohr. It draws also on recent work in AI ethics by Joanna Bryson, Luciano Floridi, and others on the question of distributed moral agency.

Key Ideas

Authorship and accountability travel together. When the first is diffused, the second is diffused with it.

Diffused accountability tends toward no accountability. The structures designed to locate responsibility cannot function without specific agents to locate.

The problem is not malice but architecture. No one designed AI collaboration to evade responsibility; the evasion is a structural consequence of distributing agency across actors of different kinds.

Existing legal and institutional structures do not yet address this. The question of who is liable when an AI-assisted output causes harm remains substantially unresolved.

The handmade artifact is not a nostalgic alternative. It is a case that clarifies what distributed authorship gives up, and what would be needed to restore.