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Manifesto on algorithmic humanitarianism

Small grocery shop in The Jungle refugee camp in Calais, 2015. Debets/Press Association. All rights reserved.

intro

  1. Artificial Intelligence (AI) is
    undergoing a period of massive expansion.
  2. This is not because computers
    have achieved human-like consciousnesss but because of advances in machine
    learning, where computers learn from training data how to classify new data.
  3. At the cutting edge are the
    neural networks that have learned to recognise human faces or play Go.
  4. Recognising patterns in data
    can be used as a predictive tool, and AI is being applied to echocardiograms to
    predict heart disease
  5. to workplace data to predict if
    employees are going to leave
  6. and to social media feeds to
    detect signs of incipient depression or suicidal tendencies.
  7. One activity that seems distant
    from AI is humanitarianism;
  8. the organisation of
    on-the-ground aid to fellow human beings in crisis due to war, famine or other
    disaster.
  9. But humanitarian organisations,
    too, will adopt AI because it seems able to answer questions at the heart of
    humanitarianism
  10. such as 'who should we save?' and 'how can we be effective at
    scale?'
  11. It resonates strongly with existing modes of humanitarian thinking
    and doing
  12. in particular the principles of neutrality and universality.
  13. The way machine learning consumes big data and produces predictions
  14. suggests it can both grasp the enormity of the humanitarian
    challenge and provide a data-driven response.
  15. But the nature of machine learning operations mean they will
    actually deepen some humanitarian problematics
  16. and introduce new ones of their own.
  17. Thinking about how to avoid this raises wider questions about
    emancipatory technics
  18. and what else needs to be in place to produce machine learning for
    the people.

maths

  1. There is no intelligence in
    Artificial Intelligence
  2. nor does it really learn, even
    though it's technical name is machine learning
  3. it is simply mathematical
    minimisation
  4. Like at school, fitting a
    straight line to a set of points
  5. you pick the line that
    minimises the differences overall
  6. Machine learning does the same
    for complex patterns
  7. it fits input features to known
    outcomes by minimising a cost function
  8. the fit is a model that can be
    applied to new data to predict the outcome
  9. The most influential class of
    machine learning algorithms are neural networks
  10. which is what startups call 'deep learning'
  11. They use backpropagation: a minimisation algorithm that produces
    weights in different layers of neurons
  12. anything that can be reduced to numbers and tagged with an outcome
    can be used to train a model
  13. the equations don't know or care if the numbers represent Amazon
    sales or earthquake victims
  14. This banality of machine learning is also its power
  15. it's a generalised numerical compression of questions that matter
  16. there is no comprehension within the computation
  17. the patterns are correlation not causation
  18. the only intelligence comes in the same sense as military
    intelligence; that is, targeting
  19. But models produced by machine learning can be hard to reverse into
    human reasoning
  20. 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

  1. Machine learning doesn't just
    make decisions without giving reasons, it modifies our very idea of reason
  2. that is, it changes what is
    knowable and what is understood as real
  3. It operationalises the
    two-world metaphysics of neoplatonism
  4. that behind the world of the
    sensible is the world of the form or the idea.
  5. A belief in a hidden layer of
    reality which is ontologically superior,
  6. expressed mathematically and
    apprehended by going against direct experience.
  7. Machine learning is not just a
    method but a machinic philosophy
  8. What might this mean for the
    future field of humanitarian AI?
  9. It makes machine learning prone
    to what Miranda Fricker calls epistemic injustice
  10. She meant the social prejudice that undermines a speaker's word
  11. but in this case it's the calculations of data science that can end
    up counting more than testimony
  12. The production of opaque predictions with calculative authority
  13. will deepen the self-referential nature of the humanitarian field
  14. while providing a gloss of grounded and testable interventions
  15. Testing against unused data will produce hard numbers for accuracy
    and error
  16. while making the reasoning behind them inaccessible to debate or
    questioning
  17. Using neural networks will align with the output driven focus of the
    logframe
  18. while deepening the disconnect between outputs and wider values
  19. Hannah Arendt said many years ago that cycles of social reproduction
    have the character of automatism.
  20. 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

  1. Of course the humanitarian
    field is not naive about the perils of datafication
  2. We all know machine learning
    could propagate discrimination because it learns from social data
  3. Humanitarian institutions will
    be more careful than most to ensure all possible safeguards against biased
    training data
  4. but the deeper effect of
    machine learning is to produce new subjects and to act on them
  5. Machine learning is performative,
    in the sense that reiterative statements produce the phenomena they regulate
  6. Humanitarian AI will optimise
    the impact of limited resources applied to nearly limitless need
  7. by constructing populations
    that fit the needs of humanitarian organisations
  8. This is machine learning as
    biopower
  9. it's predictive power will hold
    out the promise of saving lives
  10. producing a shift to preemption
  11. but this is effect without cause
  12. The foreclosure of futures on the basis of correlation rather than
    causation
  13. it constructs risk in the same way that twitter determines trending
    topics
  14. the result will be algorithmic states of exception
  15. According to Agamben, the signature of a state of exception is
    ‘force-of’
  16. actions that have the force of law even when not of the law
  17. Logistic regression and neural networks generate mathematical
    boundaries
  18. but cybernetic exclusions will have effective force by allocating
    and withholding resources
  19. a process that can't be humanised by having a
    humanitarian-in-the-loop
  20. because it is already a technics, a co-constituting of the human and
    the technical

decolonial

  1. The capture, model and preempt
    cycle of machine learning will amplify the colonial aspects of humanitarianism
  2. unless we can develop a
    decolonial approach to its assertions of objectivity, neutrality and
    universality
  3. We can look to standpoint
    theory, a feminist and post-colonial approach to science
  4. which suggests that positions
    of social and political disadvantage can become sites of analytical advantage
  5. this is where our thinking
    about machine learning & AI should start from
  6. but I don't mean by soliciting
    feedback from humanitarian beneficiaries
  7. Participation and feedback is
    already a form of socialising subjects
  8. and with algorithmic
    humanitarianism every client interaction will be subsumed into training data
  9. They used to say 'if the
    product is free, you are the product'
  10. but now, if the product is free, you are the training data
  11. training for humanitarian AI and for the wider cybernetic governance
    of resilient populations
  12. Machine learning can break out of this spiral through situated
    knowledge
  13. as proposed by Donna Haraway as a counterweight to the scientific
    ‘view from nowhere’,
  14. a situated approach that is not optional in its commitment to a
    particular context
  15. How does machine learning look from the standpoint of Haiti's
    post-earthquake rubble or from an IDP camp
  16. 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
  17. How can democratic technics be grounded in the humanitarian context?

people's councils

  1. It may seem obvious that if
    machine learning can optimise Ocado deliveries then it can help with
    humanitarian aid
  2. but the politics of machine
    learning are processes operating at the level of the pre-social
  3. One way to counter this is
    through popular assemblies and people's councils
  4. bottom-up, confederated
    structures that implement direct democracy
  5. replacing the absence of a
    subject in the algorithms with face-to-face presence
  6. contesting the opacity of
    parallel computation with open argument
  7. and the environmentality of
    algorithms with direct action
  8. The role of people's councils
    is not to debate for its own sake
  9. but the creation of alternative
    structures, in the spirit of Gustav Landauer's structural renewal
  10. An emancipatory technics is one that co-constitutes active agents
    and their infrastructures
  11. As Landauer said, people must 'grow into a framework, a sense of
    belonging, a body with countless organs and sections'
  12. as evidenced in Calais, where people collectively organised warehouse
    space, van deliveries and cauldrons to cook for 100s, while regularly tasting
    tear gas
  13. I suggest that solidarity is an ontological category prior to
    subject formation
  14. collective activity is the line of flight from a technological
    capture that extends market relations to our intentions
  15. It is a politics of becoming – a means without end to counter AI's
    effect without cause

close

  1. In conclusion
  2. as things stand, machine
    learning and so-called AI will not be any kind of salvation for humanitarianism
  3. but will deepen the neocolonial
    and neoliberal dynamics of humanitarian institutions
  4. But no apparatus is a closed
    teleological system; the impact of machine learning is contingent and can be
    changed
  5. it's not a question people
    versus machines but of a humanitarian technics of mutual aid
  6. In my opinion this requires a
    rupture with current instantiations of machine learning
  7. a break with the established order
    of things of the kind that Badiou refers to as an Event
  8. the unpredictable point of
    excess that makes a new truth discernible
  9. and constitutes the subjects
    that can pursue that new truth procedure
  10. The prerequisites will be to have a standpoint, to be situated, and
    to be committed
  11. 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
  12. On the other hand, if an alternative technics is not mobilised,
  13. the next generation of humanitarian scandals will be driven by AI.

 

Presented
at the symposium on 'Reimagining Digital Humanitarianism', Goldsmiths,
University of London, Feb 16th 2018
. More details
of the symposium can be found
here.

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