information model–data–algorithm

Originally published on nettime in reaction to an open call.


Hi Hanns and everybody,

> Rather than understanding algorithms as existing and transparent tools,
> the ALMAT Symposium is interested in their genealogical, processual
> aspects and their transformative potential. We seek critical approaches
> that avoid both mystification and commodification, that aim at opening
> the black box of “wonder” that is often presented to the public when
> utilising algorithms.

That’s very much needed. And I think there is a conceptual problem, which this conference shares with many others that talk about “the algorithm”. I agree, that the specialized field of generative art concentrates on algorithms (that generate the visual or auditive experience) and that algorithms on a larger scale matter in optimization (like b-tree sorting, fast gradient step method in pattern recognition).

However from a perspective of “gray media” (Fuller/Goffey), “logistical media” (Rossiter) on the one hand, and “habitual media” (Wendy Hui Kyong Chun) on the other, I think “algorithm” is wrong terminology. Approaching it from a perspective of the database and referring to actual practices of application programming I would argue, that algorithms are a minor issue. Of much more importance is the information model.

The information model is usually the decision, which information and subsequently data, should be included into the processable reality of computing, and what to exclude. In short: data is, what gets included according to the information model. Everything else is non-data or non-existent (under the closed world assumption) to the computer. So if you aim to look into the genealogy of algorithms, you may look into mathematics and maybe operational reserch.

You will however miss out on looking at the genealogy of _data_ and the material qualities of the _information model_. If we for instance look into how bias enters software, we usually won’t find much in algorithms. A b-tree sorting or the training of a neural network is always tied to weights, and actually needs and creates bias.

Since a computer can not understand meaning, meaning needs to be ascribed (through classification), which is done by the mentioned algorithms moving numerical weights towards a certain result that is meaningful to humans. Much more relevant for the question of bias is, how the _information model_ is organized, because it inscribes the reality of the computable.

Much more relevant is the question of how _data_ is collected, curated und used, as we can see in the current projects of Adam Harvey ( or !Mediengruppe Bitnik (, or the Data Workers Union (

I get, that ‘algorithm’ is often used as common notion, in a similar blurry way as is ‘digital’. However a stronger concern for the information model and for data would open up the avenue for a stronger political stance, since it looks into who decides about inclusion and exclusions, and how these decisions are shaped. I’m talking about identifying addressable actors who are being hold responsible. So let’s look further into the trinity: information model–––data–––algorithm (and the infrastructure in and around it).

best Francis

→ author: Francis Hunger, published on: 2019-Oct-10