Re: <nettime> Algorithms that Matter Symposium 2020: Call for contributions

[Francis Hunger <francis.hunger@irmielin.org>]

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
(https://megapixels.cc/) or !Mediengruppe Bitnik
(https://werkleitz.de/en/ostl-hine-ecsion-postal-machine-decision-part-1),
or the Data Workers Union (https://dataworkers.org/).

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


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