Can data really say anything true about us? Or is there a new zone opening up between us and our tech? Two humans have a muse
What kind of understanding of the world do we get through data - by which we mean the information that is amassed from machines watching our interactons, or observing our behaviour? If we can’t make sense of it ourselves as citizens, who should we trust to interpret the patterns they show - and how can we decide that they are trustworthy?
As we have been exploring here for a while, this stuff is far from just the domain of techies. We know from the popular sketch in Little Britain that when “computer says no”, it has consequences - for the services we access, increasingly for our medical and legal reports.
So however difficult it is, we need to start thinking about the data in our life - and what our active relationship to it should be. Websites asking you more explicitly about whether they can put “cookies” on your computer is the start of this - but we have a long way to go.
Here are two pieces which muse on how we should understand our data, but with quite different emotional overtones.
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Bill Thompson is a long-time technology journalist, and also an unusually deep thinker on the interrelationship between computers and humans.
In his blog “Slouching Towards the Digital Foundry”, Bill wonders at the blurring space between us and our machines
We have become immersed in a computational matrix that defines the modern world. Few aspects of life are untouched by the symbolic manipulation of information represented in binary. The choices we make and the constraints imposed on us are increasingly influenced or determined by the outcomes of computation, from:
the way a car responds to an accelerator
to the passport gates at an airport
to the amount of insulin released by a pump
to the news presented to us on the screens in front of us, or the buying choices offered in response
to our request to order something to cook.
This poses a number of challenges, both cognitive and emotional.
Just as nothing in the evolution of life on earth can have prepared a human being to drive a car at 80mph/130kph along a motorway (see graphic)… so nothing can have prepared us for working in a time of networked augmented intelligence.
This is a time when we are so immersed in computation that the boundaries between what our brains are processing and what our silicon augments are processing blur to the point where drawing a line is impossible. There is no point in talking about ‘virtual’ or ‘augmented’ or ‘extended’ reality, as there is just the reality of the ‘extended human”.
The technologies that have previously become embedded in our construction of the world in the past, like reading spectacles or simple amplifying hearing aids, were not malleable. Their function was defined at the time of manufacture.
Even the scientific equipment we used to explore the very far or the very small or the very dangerous was a product of physical - not logical - engineering, until relatively recently.
Today, data is acquired, and processed, and presented. But the processing is both malleable and mysterious. It is based on assumptions and models that, even when made explicit, are quickly forgotten.
For example, the backplane of the Atlas detector at CERN is designed to discard 99% of the data collected within a second. This is in order that the remaining 1% — a volume of bits previously unprecedented in scientific experimentation — can be stored for later processing.
What mysteries of the universe lay undiscovered in the immediately forgotten trove, thrown away because it was not deemed ‘interesting’ enough under the current standard model? What challenges to our current ways of thinking are never even seen because we have decided in advance that they should not be considered?
Our relationship to the the world as described by science is now almost entirely mediated by technologies that determine what should and should not be presented to their human operators. In this process, the code that runs those systems shapes the way hypotheses are tested, evidence is analysed, and worldviews are challenged.
We need to ask whether this relationship is symbiotic. Or parasitic.
Today the code running on the machines was developed, written and tested by other human beings, and the worldview embedded in that code comes at least from human bias and prejudgement. But we’re getting to the point where the systems will incorporate machine-learning models trained on data and configured in ways that are beyond human understanding.
A neural network can tell male from female by looking at the pattern of blood vessels in the iris, when no ophthalmologist can do the same - and on the basis of criteria that human operators have no access to (see paper).
So what about of the science that comes from these machines when they analyse particle collisions, pulsar emissions, or political decisions?
Something more than mere computation has been loosed upon the world, but this time we know what rough beast is slouching towards the silicon foundry to be born.
It is the soul of the new machine… The emergent force that through the fibreoptic fuse drives the algorithm. The dawn that will break behind the image sensors, as agency emerges from our craft and silicon art.
I do not think we are remotely prepared for the thing that will happen instead of general artificial intelligence. A world in which we are so immersed in computation that we will not be able to distinguish between human and machine agency — and we may not care.
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But many do care about distinguishing between these elements. In the next few weeks, we hope to engage fully with Paul Mason’s Clear Bright Future: A Radical Defence of the Human Being, his follow-up to Postcapitalism, in which he claims that being confident about what is human and not machine-like is the core of our politics over the next 20 years (see an interview on this here).
But it’s important to get away from the usual white male voices in this discussion.
Those who seek to design systems and structures on the basis of digital data, she urges, should ask deeper questions about how that data comes to them. What human categorisations are being assumed, are buried in the code?
And echoing Bill Thompson’s anxieties above, Zara wonders what insights are lost, in the data that is not collected or processed:
We speak of black boxes when it comes to the mystery choices that algorithms make, but the same could be said of the many human decisions that are made in categorising data too, whether that be choosing to limit the gender drop-down field to just ‘male/female’ as with Fitbits, or a variety of apps incorrectly assuming that all people who menstruate also want to know about their ‘fertile window’.
In large systems with many humans and machines at work, we have no way of interrogating why a category was merged or not, of understanding why certain anomalies were ignored rather than incorporated, or of questioning why certain assumptions were made.
The only thing we can do is to acknowledge these limitations, and try to use those very systems to our advantage, building our own alternatives or workarounds, collecting our own data, and using the data that is out there to tell the stories that matter to us.
In many ways, digital data is a simplification of reality, a ‘stone representation’ of a complex life. Taking this one step further: perhaps digitisation, or digital data, isn’t always the answer. Narrative histories tell us far more than a digitised family tree ever could.
The feelings that are communicated during a great oral story can never be reduced to machine-readable data.
The results of a heritage DNA test cannot reflect the life experience and history of a person — and even those results are the consequence of scientists’ preconceptions about gender and race, combined with the data they had available to learn from, codified into a digital system.
There are limits to categorisation and to digitisation, and some of those limits should act as hard stop signs for us. Digitisation is not always progress — sometimes, it’s a veneer for political systems wanting to categorise us for easier surveillance. Or an excuse that permits us to over-simplify or ignore the complexities and nuances in our lives and in our understanding of others.
Reducing ourselves to binary identities, to pre-written answers in drop-down menus, is helpful for those in power wanting to understand how to control populations, but not for those whose identities have always been at the margins.
Progress, in the grandest sense, doesn’t have to look like this. The development of humanity doesn’t have to mean outsourcing decisions to machines, or building systems that categorise us without truly understanding who we are.
Real progress should thoughtfully move us towards a more equitable and just society, not speed towards less friction while further entrenching the inequalities of the past. Our collective reluctance to put in the work necessary to acknowledge those inequities, focusing instead on easier-to-fix criteria like speed and categorisation, is simply lazy.
In the systems of the future, we need more than just fast and frictionless — we need nuance, acknowledgement of what came before, and an aspiration to make changes that improve lives for everyone, not just the most visible few.
Sometimes, complexity is a feature, not a bug.