Manifesto on Algorithmic Humanitarianism

The Manifesto on Algorithmic Humanitarianism was presented at the symposium on 'Reimagining Digital Humanitarianism', Goldsmiths, University of London, Feb 16th 2018.

Download at SocArXiv

intro

humanitarian organisations will adopt ai because it seems able to answer questions at the heart of humanitarianism
such as 'who should we save?' and 'how can we be effective at scale?'
it resonates strongly with existing modes of humanitarian thinking and doing
in particular the principles of neutrality and universality
the way machine learning consumes big data and produces predictions
suggests it can both grasp the enormity of the humanitarian challenge and provide a data-driven response
but the nature of machine learning operations mean they will actually deepen some humanitarian problematics
and introduce new ones of their own
thinking about how to avoid this raises wider questions about emancipatory technics
and what else needs to be in place to produce machine learning for the people

maths

there is no intelligence in artificial intelligence
nor does it really learn, even though it's technical name is machine learning
it is simply mathematical minimisation
like at school, fitting a straight line to a set of points
you pick the line that minimises the differences overall
machine learning does the same for complex patterns
it fits input features to known outcomes by minimising a cost function
the fit is a model that can be applied to new data to predict the outcome
the most influential class of machine learning algorithms are neural networks
which is what startups call 'deep learning'
they use backpropagation: a minimisation algorithm that produces weights in different layers of neurons
anything that can be reduced to numbers and tagged with an outcome can be used to train a model
the equations don't know or care if the numbers represent amazon sales or earthquake victims
this banality of machine learning is also it's power
it's a generalised numerical compression of questions that matter
there is no comprehensions within the computation
the patterns are correlation not causation
the only intelligence comes in the same sense as military intelligence; that is, targeting
but models produced by machine learning can be hard to reverse into human reasoning
why did it pick this person as a bad parole risk? what does that pattern of weights in the 3rd layer represent? we can't necessarily say.

reasoning

machine learning doesn't just make decisions without giving reasons, it modifies our very idea of reason
that is, it changes what is knowable and what is understood as real
it operationalises the two-world metaphysics of neoplatonism
that behind the world of the sensible is the world of the form or the idea.
a belief in hidden layer of reality which is ontologically superior,
expressed mathematically and apprehended by going against direct experience.
machine learning is not just a method but a machinic philosophy
what might this mean for the future field of humanitarian ai?
it makes machine learning prone to what miranda fricker calls epistemic injustice
she meant the social prejudice that undermines a speaker's word
but in this case it's the calculations of data science that can end up counting more than testimony
the production of opaque predictions with calculative authority
will deepen the self-referential nature of the humanitarian field
while providing a gloss of grounded and testable interventions
testing against unused data will produce hard numbers for accuracy and error
while making the reasoning behind them inaccessible to debate or questioning
using neural networks will align with the output driven focus of the logframe
while deepening the disconnect between outputs and wider values
hannah arendt said many years ago that cycles of social reproduction have the character of automatism.
the general threat of ai, in humanitarianism and elsewhere, is not the substitution of humans by machines but the computational extension of existing social automatism

production

of course the humanitarian field is not naive about the perils of datafication
we all know machine learning could propagate discrimination because it learns from social data
humanitarian institutions will be more careful than most to ensure all possible safeguards against biased training data
but the deeper effect of machine learning is to produce new subjects and to act on them
machine learning is performative, in the sense that reiterative statements produce the phenomena they regulate
humanitarian ai will optimise the impact of limited resources applied to nearly limitless need
by constructing populations that fit the needs of humanitarian organisations
this is machine learning as biopower
it's predictive power will hold out the promise of saving lives
producing a shift to preemption
but this is effect without cause
the foreclosure of futures on the basis of correlation rather than causation
it constructs risk in the same way that twitter determines trending topics
the result will be algorithmic states of exception
according to agamben, the signature of a state of exception is ‘force-of’
actions that have the force of law even when not of the law
logistic regression and neural networks generate mathematical boundaries
but cybernetic exclusions will have effective force by allocating and witholding resources
a process that can't be humanised by having a humanitarian-in-the-loop
because it is already a technics, a co-constituting of the human and the technical

decolonial

the capture, model and preempt cycle of machine learning will amplify the colonial aspects of humanitarianism
unless we can develop a decolonial approach to its assertions of objectivity, neutrality and universality
we can look to standpoint theory, a feminist and post-colonial approach to science
which suggests that positions of social and political disadvantage can become sites of analytical advantage
this is where our thinking about machine learning & ai should start from
but i don't mean by soliciting feedback from humanitarian beneficiaries
participation and feedback is already a form of socialising subjects
and with algorithmic humanitarianism every client interaction will be subsumed into training data
they used to say 'if the product is free, you are the product'
but now, if the product is free, you are the training data
training for humanitarian ai and for the wider cybernetic governance of resilient populations
machine learning can break out of this spiral through situated knowledge
as proposed by donna haraway as a counterweight to the scientific ‘view from nowhere’,
a situated approach that is not optional in its commitment to a particular context
how does machine learning look from the standpoint of Haiti's post-earthquake rubble or from an IDP camp
no refugee in a freezing factory near the serbian border with croatia is going to be signing up for andrew ng's mooc on machine learning any time soon
how can democratic technics be grounded in the humanitarian context?

people's councils

it may seem obvious that if machine learning can optimise ocado deliveries then it can help with humanitarian aid
but the politics of machine learning are processes operating at the level of the pre-social
one way to counter this is through popular assemblies and people's councils
bottom-up, confederated structures that implement direct democracy
replacing the absence of a subject in the algorithms with face-to-face presence
contesting the opacity of parallel computation with open argument
and the environmentality of algorithms with direct action
the role of people's councils is not to debate for its own sake
but the creation of alternative structures, in the spirit of gustav landauer's structural renewal
an emancipatory technics is one that co-constitutes active agents and their infrastructures
as Landauer said, people must 'grow into a framework, a sense of belonging, a body with countless organs and sections'
as evidenced in calais, where people collectively organised wharehouse space, van deliveries and cauldrons to cook for 100s, while regularly tasting tear gas
i suggest that solidarity is an ontological category prior to subject formation
collective activity is the line of flight from a technological capture that extends market relations to our intentions
it is a politics of becoming - a means without end to counter ai's effect without cause

close

in conclusion
as things stand, machine learning and so-called ai will not be any kind of salvation for humanitarianism
but will deepen the neocolonial and neoliberal dynamics of humanitarian institutions
but no apparatus is a closed teleological system; the impact of machine learning is contingent and can be changed
it's not a question people versus machines but of a humanitarian technics of mutual aid
in my opinion this requires a rupture with current instantiations of machine learning
a break with the established order of things of the kind that badiou refers to as an event
the unpredictable point of excess that makes a new truth discernible
and constitutes the subjects that can pursue that new truth procedure
the prerequisites will be to have a standpoint, to be situated, and to be committed
it will be as different to the operations of google as the balkan aid convoys of the 1990s were to the work of the icrc
on the other hand, if an alternative technics is not mobilised,
the next generation of humanitarian scandals will be driven by ai

social