Datafication, Phantasmagoria of the 21st Century
A friend sent me the photo on the left, marvelling at human ingenuity.
I also admired the photo on the left.
And then I reflected…
Everyday, nature performs feats of innovation that we will never be able to replicate.
What does this have to do with a blog about digital technologies?
It is all about what we (as a civilisation) consider as valid knowledge, what ways of knowing we TRUST, what we hold as true, what we admire.
So, digital technologies are going to save the world.
Or are they?
Let’s have a no non-sense look at how things really work.
A few comments first.
I am not a Luddite.
[Just a side comment here: Luddites were English textile workers in the 19th century who reacted strongly against the mechanisation of their trade which put them out of work and unable to support their families. Today, they have become the poster-child of anti-progress, anti-technology grumpy old bores, and “you’re a Luddite” is a common insult directed at techno-sceptics of all sorts. But Luddites were actually behaving quite rationally. Many people in the world today react in a similar fashion in the face of the economic uncertainty brought about by technological change.]
That being said, I am not anti-technology. I am extremely grateful for the applications of digital technology that help make the world a better place in many ways. I am fascinated by the ingenuity and the creativity displayed in the development of technologies to solve puzzling problems. I also welcome the fact that major technological shifts have brought major changes in how we live in the world. This is unavoidable, it is part of the impermanent nature of our worlds. Emergence of the new is to be welcomed rather than fought against.
But I am also a strong believer in using discrimination to try to make sense of new technologies, and to critically assess their systemic impact, especially when they have become the object of such hype. The history of humanity is paved with examples of collective blindness. We can’t consider ourselves immune to it.
The focus of my research (and of this post) is Datafication, i.e., the algorithmic quantification of purely qualitative aspects of life. I mention this because there are many other domains that comfortably lend themselves to quantification.
I am using a simple vocabulary in this post. This is on purpose, because words can be deceiving. Names such as Artificial Intelligence (AI) or Natural Language Processing (NLP) are highly evocative and misleading, suggesting human-like abilities. There is so much excitement and fanfare around them that it’s worth going back to the basics and calling a cat a cat (or a machine a machine). There is a lot of hype around whether AI is sentient or could become sentient but as of today, there are many simple actions that AI cannot perform satisfactorily (recognise a non-white-male face for one), not to mention the deeper issues that plague it (bias in data used to feed algorithms, the illusory belief that algorithms are neutral, the lack of accountability, the data surveillance architectures… just to name a few). It is just too easy to discard these technical, political, social issues in the belief that they will “soon” be overcome.
But hype time is not a time for deep reflection. If the incredible excitement around ChatGPT (despite the repeated urge for caution from its founder) is any indication, we are living through another round of renewed collective euphoria. A few years ago, the object of this collective rapture was social media. Today, knowing what we know about the harms they create, it is becoming more difficult to feel deliciously aroused by Facebook and co., but AI has grabbed the intoxication baton. The most grandiose claims are claims of sentience, including from AI engineers who undoubtedly have the ability to make the machines, but whose expertise in assessing their sentience is highly debatable. But in the digital age, extravagant assertions sell newspapers, make stocks shoot up, or bring fame, so it may not all be so surprising.
But I digress…
How does algorithmic technology create “knowledge” about qualitative aspects of life?
First, it collects and processes existing data from the social realm to create “knowledge”. It is important to understand that the original data collected is frequently incomplete, and often reflects the existing biases of the social milieu from where it is extracted. The idea that algorithms are neutral is sexy but false. Algorithms are a set of instructions that control the processing of data. They are only as good as the data they work with. So, I put the word “knowledge” in quotation marks to show that we have to scrutinise its meaning in this context, and use discrimination to examine what type of knowledge is created, what function it carries out, and whose interests it serves.
Algorithmic technology relies on computer-ready, quantified data. Computers are not equipped to handle the fuzziness of qualitative, relational, embodied, experiential data. But a lot of data produced in the world everyday is warm data. (Nora Bateson coined that term by the way, check The Bateson Institute website to know more, it is well worth a read). It is fuzzy, changing, qualitative, not clearly defined, and certainly not reducible to discrete quantities. But computers can only deal with quantities, discrete data bits. So, in order to be read by computers, the data collected needs to be cleaned and turned into “structured data”. What does “structured” mean? It means that it has to be transformed into data that can be read by computers; it needs to be turned into bits; it needs to be quantified.
So this begs the question: how is unquantified data turned into quantified data? Essentially, through two processes.
The first one is called “proxying”. The logic is: “I can’t use X, so I will use a proxy for X, an equivalent”. While this sounds great in theory, it has two important implications. Firstly, a suitable proxy may or may not exist so the relationship of similarity between X and its proxy may be thin. Secondly, someone has to decide which quantifiable equivalent will be used. I insist on the word “someone”, because it means that “someone” has to make that decision, a decision that is far from neutral, highly political and potentially carrying many social (unintended) consequences. In many instances, those decisions are made not by the stakeholders who have a lived understanding of the context where the algorithmic technology will be applied, but by the developers of the technology who lack such understanding.
Some examples of proxied data: assessing teachers’ effectiveness through their students’ test results; ranking “education excellence” at universities using SAT scores, student-teacher ratios, and acceptance rates (that’s what the editors at US News did when they started their university ranking project); evaluating an influencer’s trustworthiness by the number of followers she has (thereby creating unintended consequences as described in this New York Times investigative piece “The Follower Factory”); using credit worthiness to screen potential new corporate hires. And more… Those examples come from a fantastic book by math-PhD-data-scientist turned activist Cathy O’Neil called “Weapons of Math Destruction”. If you don’t have time or the inclination to read the book, Cathy also distills the essence of her argument in a TED talk, “The era of blind faith in big data must end”.
While all of the above sounds like a lot of work, there is data that is just too fuzzy to be structured and too complex to be proxied. So the second way to treat unstructured data is quite simple: abandon it. Forget about it! It never existed. Job done, problem solved. While this is convenient, of course, it becomes clear that this leaves out A LOT of important information about the social, especially because a major part of qualitative data produced in the social realm falls into this category. It also leave out the delicate but essential qualitative relational data that weaves the fabric of living ecosystems. So in essence, after the proxying and the pruning of qualitative data, it is easy to see how the so-called “knowledge” that algorithms produce is a rather poor reflection of social reality.
But (and that’s a big but), algorithmic technology is very attractive, because it makes decision-making convenient. How so? By removing uncertainty (of course I should say by giving the illusion of removing uncertainty). How so? Because it predicts the future (of course I should say by giving the illusion of predicting the future). Algorithmic technology applied to the social is essentially a technology of prediction. Shoshana Zuboff describes this at length in her seminal book published in 2019 “The Age of Surveillance Capitalism: The Fight for a Human Future in the New Frontier of Power”. If you do not have the stomach to read through the 500+ pages, just search “Zuboff Surveillance Capitalism”, you can find a plethora of interviews, articles and seminars she gave since the publication. (Just do me a favour and don’t use Google and Chrome to search, but switch to cleaner browsers like Firefox and search engines like DuckDuckGo). She clearly and masterfully elucidates how Google’s and Facebook’s money machines rely on packaging “prediction products” that are traded on “behavioural futures markets” which aim to erase the uncertainty of human behaviour.
There is a lot more to say on this (and I may do so in a later post), but for now, suffice it to say that just like the regenerative processes of nature are being damaged by mechanistic human activity, life-enhancing tacit ways of knowing are being submerged by the datafied production of knowledge. While algorithmic knowledge creation has a place and usefulness, its widespread use overshadows and overwhelms more tacit, warm, qualitative, embodied, experiential, human ways of knowing and being. The algorithmisation of human experience is creating a false knowledge of the world (see my 3mn presentation at TEDx in 2021).
This increasing lopsidedness is problematic and dangerous. Problematic because while prediction seems to make decision-making more convenient and efficient, convenience and efficiency are not life-enhancing values. Furthermore, prediction is not understanding, and understanding (or meaning-giving) is an important part of how we orient ourselves in the world. It is also problematically unfair because it creates massive asymmetries of knowledge and therefore a massive imbalance of power.
It is dangerous because while the algorithmic medium is indeed revolutionary, the ideology underlying it is dated and hazardous. The global issues and the potential for planetary annihilation that we are facing today arose from a reductionist mindset that sees living beings as machines and a positivist ideology that fundamentally distrusts tacit aspects of the human mind.
We urgently need a pendulum shift to rebalance algorithmically-produced knowledge with warm ways of knowing in order to create an ecology of knowledge that is conducive to the thriving of life on our planet.
The following post is inspired by Safron Rossi’s interview on her book about Carl Jung’s views and influence on modern astrology. In the interview, she says:
“One way to approach this point (Jung’s unique contribution) is why is Jung’s work significant in the field of psychology. And for me, I would say that it has to do with the way he attempted to meld together the wisdom of the past with modern psychological understanding and methods of treatment.
The Jung psychology is one that grows organically from traditional understandings, particularly in the realms of spirituality, religion, mythology, and comparative symbolism. And in an era where psychology was becoming increasingly behavioural and rationalistic, Jung insisted on the importance of a spiritual life because that has been the core of the human experience from time immemorial. Why all of a sudden would the spiritual life really not be so important? It’s a really big question.”
What she mentions is central to the argument of my PhD. Suddenly, in the 19th century, at the time of the industrial revolution, the tacit experience and understanding of living became not so important, or rather, not so reliable as a way of knowing. The belief that emotions are clouding the (rational) mind and that the machine was more reliable than humans because it had no messy emotions became the mainstream ideology.
But tacit knowing (i.e. the qualitative knowing that results from embodied experience and which can also be called intuitive knowing) is a fundamentally feminine way of knowing. Instead with the Industrial Revolution, it has been replaced with faith in masculine ways of knowing, so called scientific, but in fact, “mechanistic” more than “scientific”.
As Mikhail Polanyi argues in his books Personal Knowledge (1958) and The Tacit Dimension (1966), tacit knowing is fully part of science. What I call the statistical mindset is a reductionist, mechanistic way of knowing that solely has faith in mechanistic, explicit and importantly, measurable knowledge.
Here, Rossi says that Carl Jung gave (feminine) tacit knowing a place in modern psychology at a time (the time of the industrial revolution) when disciplines such as psychology and sociology were overwhelmed by the statistical mindset that values measurability above all. Examples of this in the field of psychology is the behavioural school, in sociology, Auguste Comte and positivism.
In Europe, the 19th century was the century when women were believed to be too irrational to make important decisions (like voting for example) and it was also the century when purely statistical, measurable pseudo sciences (e.g., the dark science of eugenics) were born; it was the time when the factory line became the model for everything, mass production, but also the health system, the economy, psychology, education etc…
It is important to realise that the rationalisation of the social sciences was not in and of itself a “bad” thing. In a way, it was also a way to bring some degree of rigour to the field, and more importantly, to experiment with what can and cannot be measured. Walter Benjamin talked about the Phantasmagoria of an age, i.e., the set of belief system that underlies the development of thought during that period of time. Measuring, fragmenting the whole into parts, analysis, control over the environment were all part of the phantasmagoria of the Industrial Revolution and the Modern Age. All disciplines went through this prism (including Design, I may do a post on this later). Jung melded WISDOM into MODERN PSYCHOLOGY, which was very unusual at the time.
Statistical knowledge is predictive knowledge. We use statistics to know something that will happen in the future, like the likelihood of a weather event to happen, or market movements, or usage of public transport etc… It is the best knowledge we have to OPTIMISE, when the values of EFFICIENCY and convenience are primordial (like in urban or business planning for example). It is founded on the masculine principle trait of linear logic (if A and B, then C), and on the equally masculine principle trait of goal orientation (Jung’s definition of masculinity: know what you want and how to go and get it).
This is not in and of itself bad or good, there is no value judgement here. Again, it is not a matter of superiority (which is a masculine concept, i.e., fragmenting and analysing by setting up hierarchies), but of BALANCE. Today, we live in a world (more specifically, the geographies at the centre of power) where feminine ways of knowing, which emphasise regeneration, intuitive insights, collaboration, inter-dependencies and relationality are not trusted and are suppressed, often in the name of science.
Living systems function on the principles of feminine ways of knowing. But it is not really science itself that smothers feminine ways of knowing, it’s the reductionist mechanistic mindset (and the values of efficiency and optimisation) which is applied to areas of life and of living experience where it has nothing to contribute.
As I argue in the PhD, while digital technologies are indeed revolutionary in terms of the MEDIUM they created (algorithmic social platforms), from the point of view of the belief system that underlies them, they in fact perpetuate an outdated mindset (described above) which serves the values of efficiency and optimisation with a disregard for life.
A few words from John Thackara (who wrote the afterword of Chris Jones “Designing Designing”) on Chris Jones’ mission and philosophy (the full post can be found on Thackara’s blog).
“As a kind of industrial gamekeeper turned poacher, Jones went on to warn about the potential dangers of the digital revolution unleashed by Claude Shannon.
Computers were so damned good at the manipulation of symbols, he cautioned, that there would be immense pressure on scientists to reduce all human knowledge and experience to abstract form.
Technology-driven innovation, Jones foresaw, would undervalue the knowledge and experience that human beings have by virtue of having bodies, interacting with the physical world, and being trained into a culture.
Jones coined the word ‘softecnica’ to describe ‘a coming of live objects, a new presence in the world’. He was among the first to anticipate that software, and so-called intelligent objects, were not just neutral tools. They would compel us to adapt continuously to fit new ways of living.
In time Jones turned away from the search for systematic design methods. He realized that academic attempts to systematize design led, in practice, to the separation of reason from intuition and failed to embody experience in the design process.”
All of the above ring very true today. The reductionist approach to knowledge, the general disdain for the richness of human knowledge and experience, the widespread contempt for embodied knowledge, the radical separation of reason and intuition, the hidden shaping of a new belief system around the superiority of rational machines, the invisible but violent bending of human friendly ways of living to fit machine dominated new ways of living.
My main argument is that that datafication is the phantasmagoria of the 21st century, the same way mass consumerism was the phantasmagoria or the dream of the 20th century. My inspiration is the work of Walter Benjamin, The Arcades Project.
I am defining datafication as the quantification of qualitative aspects of life, i.e. human experience generally.
I am arguing that this phantasmagoria is creating a massive epistemological shift towards a more impoverished type of knowledge because in this massive enterprise of quantification, what cannot be turned into computer data or in other words what cannot be quantified is just abandoned. And now that algorithms are making decisions in most areas of life such as education finance justice and so on this quantification has a direct impact on the system as we live in.
A musing on reductionism, the type of thinking at the root of datafication, after an exchange with a friend on the topic.
He, rightfully I believe, mentioned that there is a place for reductionist thinking, it is useful and even essential for many tasks. The problem starts when we think of it as the path to truth.
I agree.
My issue with reductionism is not that it is useless or “bad” (for lack of a better word) in and of itself, but that, in the datafied society of the early 21st century where algorithms have taken over decision-making in many areas of life, it has become (or is fast becoming) the only valid source of knowledge. What can’t be reduced to computer data is for the most part abandoned. In other words, be subjugated or be forgotten.
As a society, we bask in the warmth of the belief about the innate progress inherent to the digital revolution, and we self congratulate for having left a boring 20th century behind. But the type of thinking underscoring the digital “revolution” comes straight from the 19th century, so where is the revolution? It is the pinnacle of the logico-linear engineer type of thinking. I have nothing against engineers, they have an important place and role to play in our societies, but when this type of thinking colonises all areas of life and all dimensions of humaneness, and suppresses other ways of seeing and being in the world, I say Houston, we have a problem.
Neil Postman (one of my favourite authors in the field of media studies, in many ways a visionary) touches upon this idea in his book “Technopoly: The Surrender of Culture to Technology”, a must-read! In a technopoly, the ideology underlying the technological tools becomes self-justifying and it is the technology that provides guidance to society instead of the other way round.
“Technopoly: The Surrender of Culture to Technology is a book by Neil Postman published in 1992 that describes the development and characteristics of a “technopoly”. He defines a technopoly as a society in which technology is deified, meaning “the culture seeks its authorisation in technology, finds its satisfactions in technology, and takes its orders from technology”. It is characterised by a surplus of information generated by technology, which technological tools are in turn employed to cope with, in order to provide direction and purpose for society and individuals.” [Wikipedia]
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