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.
Bruce Schneier’s blog “Schneier on Security” often presents thought-provoking pieces about the digital. This one directly relates to the core question of my PhD about the shifting nature of reality in the digital age.
A piece worth reading. You can also browse through the comments on his blog.
Schneier’s self intro on his blog: “I am a public-interest technologist, working at the intersection of security, technology, and people. I’ve been writing about security issues on my blog since 2004, and in my monthly newsletter since 1998. I’m a fellow and lecturer at Harvard’s Kennedy School, a board member of EFF, and the Chief of Security Architecture at Inrupt, Inc.”
The MarkUp (an investigative publication focusing on Tech) has released an investigation into the Wisconsin’s state algorithm used to predict middle school students’ dropout before they graduate from high school.
Read the whole story here.
The algorithm is called The Dropout Early Warning System (DEWS). Students dropout is an important issue that needs to be addressed. Improving the chances of students staying in school and graduating from high school is a laudable goal. The question is: are algorithms reliable tools to do so? As it happens, it seems that they are not.
DEWS has been used for over a decade. The data used to create scores includes test scores, disciplinary records, and race. Students scoring below 78.5% are marked as High Risk (and a red mark appears next to their name). The MarkUp reports that comparisons between DEWS prediction and state records of actual graduations show the system is wrong three quarter of the time, especially for Black and Hispanic students. In other words, the system invalidates the very purpose for the system to exist.
Even more telling: the Department of Public Instruction (DPI) ran its own investigation into DEWS and concluded that the system was unfair. That was in 2021. In 2023, DEWS is still in use. Does this mean that our over-reliance on algorithmic systems has created a situation where we know they fail us, but we have no credible alternative, so we keep using them?
I am reminded of the seminal book by Neil Postman “Technopoly”. He says that in a Technopoly, the purpose of technology is NOT to serve people or life. It justifies its own existence merely by existing. In a Technopoly, technology is not a tool, “people are the tools of their tools” (p68). More importantly, and more problematically, “once a technology is admitted, it plays out its hand; it does what it is designed to do. Our task is to understand what that design is” (p128). It is safe to say that digital technologies have been admitted, but do we really have any understanding of what their design is?
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.
This is a short abstract of my PhD research. I will post more details in the coming days and weeks.
I first look at the epistemological processes behind datafied knowledge and contrast them with the processes of tacit knowledge production. I extract 5 principles of tacit knowledge and contrast them to 5 principles of datafied knowledge, and I contend that datafied knowledge is founded on a reductionist ideology, a reductionist logic of knowledge production, reductionist data and therefore, produces a reductionist type of knowledge. Instead of helping us to understand the world we inhabit in more systemic, holistic and qualitative ways, it relies essentially on quantitative, disembodied, computationally structured computer-ready data, and algorithmically optimised processes.
Through the filter of Walter Benjamin’s work “The Arcades Project”, I argue that datafication (defined as the quantification of the qualitative aspects human experience) is a Phantasmagoria, a dream image, a myth, a social experience anchored in a culture of commodification. The digital production of knowledge is supported by a need to reduce uncertainty and increase productivity and efficiency. It essentially serves a predictive purpose. It does not help us to understand the intricate, beautiful, fragile, qualitative, embodied experience of being alive in a deeply interconnected and interdependent world, an experience that to a great extent, defines humaneness and life in general. In this sense, datafied knowledge is hostile to life.
Finally, I call for a rebalance between tacit and datafied ways of knowing, and a shift to a more regenerative ecology of knowledge based on the principles of living systems.
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.
This is the abstract of my PhD submitted in August 2022
As qualitative aspects of life become increasingly subjected to the extractive processes of datafication, this theoretical research offers an in-depth analysis on how these technologies skew the relationship between tacit and datafied ways of knowing. Given the role tacit knowledge plays in the design process, this research seeks to illuminate how technologies of datafication are impacting designerly ways of knowing and what design can do to recalibrate this imbalance. In particular, this thesis is predicated on 4 interrelated objectives: (1) To understand how the shift toward the technologies of datafication has created an overreliance on datafied (i.e., explicit) knowledge (2) To comprehend how tacit knowledge (i.e. designerly ways of knowing) is impacted by this increased reliance, (3) To critically explore technologies of datafication through the lens of Walter Benjamin’s work on the phantasmagoria of modernity and (4) To discover what design can do to safeguard, protect and revive the production of tacit knowledge in a world increasingly dominated by datafication.
To bring greater awareness into what counts as valid knowledge today, this research begins by first identifying the principles that define tacit knowledge and datafied ways of knowing. By differentiating these two processes of knowledge creation, this thesis offers a foundation for understanding how datafication not only augments how we know things, but also actively directs and dominates what we know. This research goes on to also examine how this unchecked faith in datafication has led to a kind of 21st century phantasmagoria, reinforcing the wholesale belief that technology can be used to solve some of the most perplexing problems we face today. As a result, more tacit processes of knowledge creation are increasingly being overlooked and side-lined. To conclude this discussion, insights into how the discipline of design is uniquely situated to create a more regenerative relationship with technology, one that supports and honours the unique contributions of designerly ways of knowing, are offered.
Fundamental principles framing Grounded Theory are used as a methodological guide for structing this theoretical research. Given the unprecedented and rapid rate technology is being integrated into modern life, this methodological framework provided the flexibility needed to accommodate the evolving contours of this study while also providing the necessary systematic rigour to sustain the integrity of this PhD.
Keywords: datafication, tacit knowledge, phantasmagoria, regeneration, ecology of knowledge
Sunday Morning Musings on Raising Consciousness and Spiral Dynamics®.
I always have a problem with the term “raising consciousness”; first because there’s something subtly arrogant and hubristic about it, it presupposes that A) I, as a person, know exactly at what level everybody else is (rather unlikely), and that B) some are below me and they need to be lifted to my level. 😅😔 This is the vertical hierarchy of values underlying the mentality of colonisation, eugenics and commodification. God at the top, me and those like me just below, and the rest needing to be enlightened (or exploited) below me.
But also because it implies a view of the world that is imbued with the idea of infinite progress. This idea is so deeply pervasive to the western civilisation that we do not even question its validity. It’s important to do so though, because infinite progress is also the idea that validates a related concept: infinite growth. But while a beautiful concept, infinite progress is as unlikely as infinite growth. Progress is not a core idea to eastern philosophies or indigenous wisdom.
This goes back to the core of the Spiral Dynamics® model and how it’s been incorporated in philosophies and ideologies that have progress as their core value. As I understand it, Clare Graves developped his ECLET model not out of a concern with moving humanity up the hierarchy of values. He was more concerned about alignment within each level. His enquiry happened during a period of time when Maslow’s work became mainstream and the pyramid became an icon, but his question was very different. His driving metaphor was not the pyramid (a useful but somewhat basic shape). He was focusing on complexity, and more precisely, on the alignment between complexity in the environment and the capacity to deal with that level of complexity in one’s mind.
To reflect this balance, he did not use colours (which simplify but obfuscate important aspects of the purpose) but a set of two letters to describe the levels. AN for beige, BO for purple, CP for red, DQ for blue, ER for orange, FS for green, GT for yellow and HU for turquoise. One letter represented level of complexity in the environment and the other ability to handle complexity. “Capacity to handle complexity” is absolutistic for DQ (either-or, good/bad, us/them), pluralistic for ER (there is a range of different possibilities and I choose what’s the best one for me), contextual for FS (it all depends on context) and probabilistic for GT. He also said that from his research (and the research of some of his students after his death) very (VERY) few people were truly aligned at the second tier although higher tiers are attraction points for personal projections from lower levels. In other words, from an ER point of view, GT looks extremely sexy, and DQ will tend to see oneself as FS.
He wrote that a person would lead a more coherent and more fulfilled life if he or she was aligned at their level, regardless of where that level stood in the hierarchy of value. This model underlies his theory of change: when someone whose ability to handle complexity is thrown into a more complex environment, there is a transition period to adapt to the new levels of complexity. Similarly, one can be thrown to a lesser complex environment by life circumstances (say in the case of civil war for example when survival becomes key), and one’s ability to handle complexity can also go from more to less (as in the case of illness affecting cognitive faculties for example). There was no inkling of the desirability of a vertically upward moving progress in his work, and no mention of consciousness. For him progress was synonymous with alignment. It’s only later that his model was simplified into colours and it became easy to integrate into an integralist view of the world that takes vertical upward progress as its core value.
So, I would propose that we need new metaphors and a new vocabulary to replace “raising consciousness” which presupposes a vertical upward moving hierarchy. Metaphors and language that flatten vertical hierarchies into multidimensional complex networks. Fractals instead of pyramids. And then (and this is where the hard work begins! 😅😜), we need to fully integrate those metaphors and language, to get so familiar with them that they become like a limb, a full part of us and how we see the world. And maybe then, only then, will we have opened our “consciousness” enough to realise that what we projected onto the world was all within ourselves. Until then, it is probably safer to see ourselves on the less evolved side of the spectrum. 🙃
This is the first part of a video presentation at IASDR 2021 on Shifting Ways of Knowing in the Digital Age.
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