Objectivity

Yesterday, I came across a nature-documentary-like video showing oxpeckers eating ticks off a buffalo, with a David Attenborough-like voiceover. And while oxpeckers do eat ticks off of buffalo in the savannah, and the voice-over is factually correct in its descriptions, the video itself is AI-generated (at which point I stop watching it). Who is to say this seemingly well-intentioned "educational" video is objective or bears any resemblance to reality?

In the book Objectivity, Daston and Galison study the history of objectivity as a concept and discuss how it evolved over time, driven by changes in technology, philosophy, and shifting standards within scientific communities of what counted as rigorous, and what counted as the scientist's subjectivity getting in the way.¹ They do this through analysis of scientific atlases: botanical catalogues, anatomical diagrams, and crystallography plates. Such an atlas has two aims: pedagogy and accuracy. To communicate the "thingness" of a phenomenon, the image has to be clear enough to teach and faithful enough not to lie, and those two demands can pull against each other. D&G's point is that even seemingly abstract concepts like objectivity can be studied through the practices around them: institutions, procedures, material constraints, and ethical attitudes that govern scientific image-making.

In the 18th century, atlases tried to depict ideal forms: the orchid in full flower, not as a single real-world instance sampled as-is, even if that reflects only a fraction of its total existence, not broken or bug-eaten but in perfect condition, a non-existent ideal. The cataloguer and sketcher decided what counted as ideal. D&G call this truth-to-nature: the expert deciding which specimen best represents the type because they understand the phenomenon deeply enough to make that call.

The cataloguer's judgment was what gave the atlas its authority, but it was also what made it vulnerable. If the botanist decided which specimen was ideal, which angle was most representative, which imperfections to omit, the atlas was as much a record of the botanist's judgment as of the plant itself. The photograph seemed to solve this: a mechanical process with no human interference, and one with indexicality, the image tracing the thing itself. D&G call this mechanical objectivity. And once photography existed, the idealized drawing became insufficient.

But a perfectly faithful image of one specimen in bad light, at an odd angle, half eaten by insects, is accurate and useless. The trained expert still had to decide which photograph to include, what it should show, and whether it was representative or an outlier. D&G call this trained judgment, irreducible, because no image selects itself. The photograph moved the judgment from creation to curation.

Which brings me back to the oxpeckers eating bugs off a buffalo. In terms of truth-to-nature, it is a diffusion model's statistical average of what such a scene probably looks like, from camera angles impossible to document. More data might help: a better average gets closer to the range of what the thing actually looks like, maybe fine for an infographic. But it presents itself with the surface of mechanical objectivity, photorealist, immediate, the way footage looks, carrying the indexical signal that this was captured, while operating as a hollowed-out truth-to-nature, averaging toward the typical scene without the reasoned expert judgment that legitimized truth-to-nature, and no amount of better training changes what that symbol means to the viewer. The depiction might even be accurate, but that's almost beside the point; the medium is making a claim the artifact can't support. Moreover, it has no trained judgment, because that requires understanding what the image is of, not just what images statistically tend to look like.

And unlike an atlas, where many individuals can audit each other's reasoning and debate what belongs, the reasoning inside a diffusion model isn't transparent in any comparable way. When the botanist chose which orchid to draw, that choice was legible, arguable, and correctable. Here it isn't, at least not yet.²

This also applies to a lot of historical slop I see everywhere, depictions of historical figures where no truth-to-nature can be argued for at all, and the best we have is a recreation worked backward from portraits of that era, themselves idealized, themselves curated. For historical figures, there was never indexicality to begin with, and the AI depictions don't seem to acknowledge what is being used to fill that gap. At least the oxpecker is real and could be filmed. A survey of Western European castle architecture I've been reading, consisting of idealized sketches alongside photographs of real sites, shows how trust is built differently across disciplines, through varying composites of indexicality, curation, and trained judgment. A photorealist AI video of Charlemagne has none of these.

Whether any of this is causing a broader renegotiation of what people consider objective, not in journals, but for the Instagram reel scroller, given how widely shared some of these are, I think it probably is. When the threshold for "accurate enough" varies across viewers and the image carries no record of how it was made, there is no shared ground on which to have that disagreement.

Footnotes

1. Thanks to Professor Morar for introducing me to the book. He has also written about cartographic atlases as artifacts for understanding how geographic knowledge was transmitted between China and the West. ↩

2. Though there is ongoing work on making the internal representations of such models more interpretable, which, if it matures, could change this in ways that are genuinely interesting (Lindsey et al., 2024). ↩