AI as a Mirror

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Review of S. Vallor (2024). The AI Mirror. How to Reclaim Our Humanity in an Age of Machine Thinking. Oxford University Press, New York, 263 p.

Dr. G.J. van Bussel

Shannon Vallor is a professor of the ethics of data and artificial intelligence (AI) at the University of Edinburgh. In this role, she explores how new technologies such as AI, robotics, and data science are reshaping human moral character, cognitive habits, and social practices. As one of today’s leading philosophers of technology, she is a key figure in contemporary debates about humanity’s technological future. Her 2016 book, Technology and the Virtues, is an attempt to rethink virtue ethics in the context of technological upheaval and breakthroughs. In it, she presents ‘technomoral virtue ethics’, a framework that critiques how technological breakthroughs erode traditional moral capacities, while also proposing strategies for cultivating new virtues adapted to our era of technological disruption. [i] Her latest book, The AI Mirror (2024), builds on this research and focuses on three interconnected themes: the moral agency of AI systems; the effects of algorithms on human self-understanding; and the conditions needed for responsible innovation. In The AI Mirror, Vallor provides a moral and philosophical analysis of AI and its role in shaping knowledge, values, and morality.

Nearly a year after publication, The AI Mirror remains relevant, its title perfectly expressing Vallor’s central thesis: what we call ‘AI’ is not intelligence but a reflection — one that mimics human cognition while fundamentally lacking understanding. The mirror analogy proves very powerful, exposing how these systems merely process and refract historical data imbued with human conceptions (and misconceptions) including conscious or unconscious biases and misrepresentations of reality. [ii] AI serves as a mirror that exposes and intensifies societal biases, aspirations, and moral contradictions. Vallor argues that AI not only mirrors but recursively influences human cognition.

Employing the mirror metaphor, she warns that without deliberate human intervention, AI will perpetuate biases, distortions, and blind spots rather than fostering meaningful progress. These systems ‘aren’t designed to be accurate — they are designed to sound accurate’ (p. 121.) This distinction is vital, highlighting how large language models trade in plausibility rather than truth, and how their outputs, despite their fluency, remain cognitively hollow. Vallor asserts that the self-image projected by AI is deceptive and that an uncritical fascination with AI poses significant dangers. ‘It is their power to induce in us a type of self-forgetting — a selective amnesia that loosens our grip on our own human agency and clouds our self-knowledge. It is an illusion that can ensnare even the most technologically adept among us’ (p. 2.)

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Ideological Platforms

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Book review of: M. Pepi (2025). Against Platforms: Surviving Digital Utopia, Melville House, Brooklyn-London, 224 p.

Dr. G.J. van Bussel

After the publications of Srnicek’s Platform Capitalism (2016) and Zuboff’s Surveillance Capitalism (2019), there has been an exponential increase in the number of critiques of large technological firms and their platforms. [i] Concurrently, however, digital platforms have consolidated their influence over global societies. In Against Platforms, Mike Pepi, advances the argument that such platforms are not impartial, but rather ideological instruments that exert a significant influence on social, artistic, and political life. Pepi contests the techno-utopian perspective that platforms serve as instruments for ‘good’, for democratization, and equality. According to Pepi, digital platforms exhibit an extractive character, a phenomenon that has the effect of eroding autonomy, under the guise of promoting connectivity.

Digital platforms were initially presented as a ‘utopia’, an open space where the free flow of information would ensure a democratic and egalitarian society. Michael Pepi has expressed the opinion that this idea is misguided, at least in its current form. It is:

‘an ideology run amok. Techno-utopianism—the idea that technology, and technology alone, will create a more egalitarian, democratic society—has been around since we have had tools to make labor easier. But this utopia, like all utopias, doesn’t really exist. Despite this misguided attempt, techno-utopianism has effectively synthesized with two other powerful ideologies—techno-determinism and free-market capitalism—to create what many refer to as platform capitalism.’ (p. 2)

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The Decay of Culture?

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Review of C. Rosen (2024). The Extinction of Experience. Being Human in a Disembodied World, W.W. Norton & Company, New York, 272 p.

Have you ever read a book that gave rise to almost as many notes as in the book itself? This is one of them. To be interested in a new approach and to end up very disappointed by the way the book is structured, by its anecdotal character without evidence telling a different story than the book’s title suggests, by the many contradictions, the sloppy research, the repetitive lamentations about disappearing phenomena, the extreme interpretations and one-sidedness of the narrative, and the fact that there are no suggestions for solutions.

In The Extinction of Experience, Christine Rosen eloquently attempts to explain how technology is reshaping the human ‘experience’. She wants readers to think about what it means to be human in a world where digital media seem to overwhelm and take full possession of human attention. Rosen is concerned about what it means to be human when ‘experience’ is hijacked by virtual analogues and becomes disembodied and, more worryingly, monetized. Experiences are, according to Rosen, practices that formed us as individuals, groups, and societies.

“Experiences, broadly considered, are the ways we become acquainted with the world. Direct experience is our first teacher. As we learn to navigate the world around us, we attach meaning to what we encounter. These experiences and meanings differ across times and cultures but share common threads. Different people speak different languages and have different social customs, but the fact that we all have languages and customs is a marker of our shared humanity. Certain types of experience — some rooted deeply in our evolutionary history, such as face-to-face interaction and various forms of pleasure-seeking; others more recent and reflective of cultural norms, such as patience and our sense of public space and place — are fading from our lives. Many of these experiences are what, historically, have helped us form and nurture a shared reality as human beings.” (introduction)

Rosen focuses on what digital platforms have given us, but more importantly, what they have suppressed. Four themes are continuously repeated: the commodification of experience, the collapse of public communal places, the dangers of frictionless interaction, and the need to reclaim ‘embodied’ experiences.

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Tearing us Apart

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Book review of: N. Carr (2025). Superbloom. How Technologies of Connection Tear Us Apart, W. W. Norton & Company, 272 pages.

The question of whether the world has become a better place since the arrival of ‘technologies of connection’, as Nicholas Carr calls them, is a complex one. These technologies, which include all forms of hardware and software designed to facilitate connectivity between individuals, such as phones, computers, tablets, internet browsers, social media platforms, and large language models, have become an integral part of our lives. However, the potential implications of these technologies for democracy, as well as the dissemination of misinformation, hallucinations, and deepfakes that threaten societal norms and values, ethical considerations, and democratic plurality, appear to contradict the naïve idealism expressed in Silicon Valley’s manipulative marketing. However, the advent of GPS has facilitated global navigation, enabling individuals to communicate with others, even in remote locations, and to access and stream a vast array of audio and video content. It is a mixed bag, with many advantages for the individual and a lot of disadvantages for society. [i]

Nicholas Carr has published extensively about business, information technology and culture, and has expressed highly critical views on the effects of these on individual users and society at large. His book The Shallows. What the Internet Is Doing to Our Brains was a finalist for the 2011 Pulitzer Prize in General Nonfiction. In this provocative book, Carr advanced the argument that Google and the internet are ‘making us stupid.’ It established Carr as a leading commentator on the Internet and its societal consequences. In my review of the book (in Dutch), I stated the following:

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To Control History

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Book review of: J. Stanley (2024). Erasing History. How Fascists Rewrite the Past to Control the Future, Atria/One Signal Publishers, New York, 256 pp.

In this book, the American philosopher and Yale University professor, Jason Stanley, explores the practice of governments manipulating the past to shape a future that aligns with their desired vision. This is a very common practice, even in democratic societies. Uncomfortable truths are erased, certain books are banned from schools, and accepted history is replaced with myths, thereby justifying the power of the governing elites. To succeed, it is necessary for these elites to ascertain that the populace is unaware of what really happened. [i] Especially in authoritarian regimes, it is important to control narratives about the past.

Studying history is dangerous. The act of examining the past from multiple vantage points gives rise to a societal depiction that is characterized by historical plurality, diversity, and openness to reinterpretation. These reinterpretations, in turn, give rise to novel perspectives that serve to augment the historical plurality that is already extant. Within democratic societies, this plurality serves to nurture principles of equality, collective reflection, and the acceptance of a multitude of historical narratives. Each of these narratives offers a distinct perspective on truth and reality, thereby contributing to a rich tapestry of historical understanding. Conversely, within authoritarian states, history is often simplified and mythologized into a single, overarching narrative, serving to justify the supremacy of specific social groups or segments.

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Overstating a Case

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Review of: J. Haidt (2024). The Anxious Generation: How the Great Rewiring of Childhood is Causing an Epidemic of Mental Illness, Penguin Press, 400 pp.

I have read the book on two occasions: first when it was first published, and most recently in the last two weeks. It is a disturbing, thought-provoking, controversial and misleading book.

The book is (and continues to be) a bestseller. It has received a great deal of media attention, and it has been translated into many languages. It examines the rise in mental health issues among the generation born after 1995. Haidt’s central thesis is what he calls ‘the great rewiring of childhood’ that fundamentally changed the experiences of young people and that led to an epidemic rise of anxiety, depression, and other mental health problems. His basic premise is twofold: first, that the introduction of smartphones and the subsequent widespread use of social media has transformed a ‘play-based’ childhood into a ‘phone-based’ one, leading to social and sleep deprivation, attention fragmentation, and addiction, and, second, that ‘parental overprotection’ in the real world is matched with ‘parental underproduction’ in the virtual world, leading to youths being confronted with the negatives of social media, like porn, cyber bullying, dangerous challenges as well as ideal types that can never be met. Haidt contends that all of this led to a collective ‘rewiring’ of the young mind, resulting in increased rates of anxiety, depression, self-harm, and (thoughts of) suicide.

Haidt’s critique focuses on social media platforms, or social networking sites, which he argues are characterized by one-to-many and many-to-one communication. This communication predominantly focuses on the ‘best sides’ (or idealized sides) of user lives to groups of unknown others. Haidt expresses particular concern regarding the use of such platforms by young people (defined as individuals under the age of 18) and asserts that the purported benefits of social media, such as enhanced information management and reputation building, are of negligible relevance to this demographic. Instead, he contends that younger users, particularly those between the ages of 10 and 14, primarily employ social media for the purpose of entertainment. The fact that their brains (particularly the regions involved in regulating behaviour) are not yet fully developed means that this aspect of social media platforms has the potential to become addictive, due to the difficulty of discontinuing usage. Haidt posits that contemporary social media platforms are problematic due to several factors, including the prevalence of endless scrolling, push notifications, the emphasis on enticing video content, and personalization. In my opinion, notwithstanding the tendency to reach premature conclusions given the data and analysis presented, Haidt’s thesis does possess a degree of credibility. Social media is a far more compelling and engaging form of entertainment than reading books or watching television. It is a well-documented fact that young people never dedicated 30-35 hours per week to reading or watching television.

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The Unaccountability Machine

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Book review of: D. Davies (2024). The Unaccountability Machine. Why Big Systems Make Terrible Decisions — and How the World Lost Its Mind, Profile Books, 304 pp.

A passenger is refused permission to board an aircraft and makes a plea to a sympathetic flight attendant. Regrettably, the passenger’s grievance pertains to a company policy, and the flight attendant is unable to assist. This scenario exemplifies a scenario in which the onus of decision-making is entrusted not to an individual, but rather to a set of predetermined guidelines.

‘The unsettling thing about this conversation is that you progressively realise that the human being you are speaking to is only allowed to follow a set of processes and rules that pass on decisions made at a higher level of the corporate hierarchy. It’s often a frustrating experience; you want to get angry, but you can’t really blame the person you’re talking to. Somehow, the airline has constructed a state of affairs where it can speak to you with the anonymous voice of an amorphous corporation, but you have to talk back to it as if it were a person like yourself.’ (p. 16)

Davies poses the question of why errors and crises never appear to be attributable to any specific individual or entity. The blame is often attributed to ‘the system’, ‘the computer’, ‘the algorithm’, ‘the AI’, or similar entities. Davies terms this phenomenon an ‘accountability sink’, a structural element that absorbs or obscures the consequences of a decision, thereby preventing direct accountability.

The phenomenon of accountability sinks, as postulated by Davies, serves to impede individuals from both willing and unwillingly making or changing decisions and, consequently, being held directly accountable for them.

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The escalating water use of AI

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Despite efficiency gains in other areas, the exponentially growing demand for AI has led to an increase in the water footprint.

Driven in part by the growth in AI, Google’s scope-1 onsite water consumption (cooling) in 2022 increased by 20% compared to 2021, and Microsoft saw a 34% increase over the same period. The majority of water consumption for server cooling is derived from potable sources.

Additionally, Scope-2 offsite water consumption (electricity generation) is experiencing growth because of AI’s enormous use of computing power. Scope-3 water consumption within the AI supply chain is also mounting. For instance, the fabrication of a single microchip necessitates approximately 2,200 gallons of ultra-pure water. The staggering demand for AI processing necessitates the utilization of thousands of these chips.

The escalating water usage associated with AI is a matter of significant concern. We need to address water scarcity, a pressing global concern exacerbated by a rapidly expanding population, dwindling water resources, and ageing water infrastructures. This concern extends beyond the mere magnitude of AI models’ water usage, underscoring the imperative for developers to proactively address the shared global challenge of water scarcity.

Shaolei Ren elucidated this challenge in: ‘How much water does AI consume?’. See here.

I have earlier mentioned this issue in ‘The ecological footprint of Artificial Intelligence’.

Published in a LinkedIn post, December 2024.

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The data’s afterlife

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Have you ever thought about your data’s afterlife? The digital footprints that we leave behind after our demise are so extensive that our identities can persist beyond our mortal existence.

The advent of AI has enabled the possibility to interact with departed loved ones. A young man is still able to compete with his father in an old Xbox racing game, utilizing his father’s saved games. The ‘digital afterlife industry’ provides a posthumous delivery of video, audio, and written messages. The use of AI enables the replication of language patterns and personality traits through the analysis of extensive personal data.

In The Afterlife of Data. What Happens to Your Information When You Die and Why You Should Care, Carl Öhman, an assistant professor at Uppsala University, addresses the issue of data ownership and the decision-making process regarding the fate of our digital assets after our demise. Who assumes control will effectively control future access to this digital past. This information (or substantial portions thereof) will persist, barring the dissolution of Meta (Facebook) and Alphabet (Google) or a conflict that results in the destruction of the data centres used to store these data.

The afterlife of data raises a multitude of ethical, economic, and legal inquiries that are currently unaddressed. The concept of a legal right to privacy does not extend to the deceased. Secrets that were previously thought to be confined to the grave can, in some cases, have a detrimental impact on the reputation of the deceased. In theory, individuals have the option to delete their data, at least any materials they do not wish to be accessible to the general public. Data, however, will endure. Some parents have attempted to access and process the data of their deceased child, but have been unsuccessful. The courts have determined that it is not within their purview to make such decisions, leaving it to the platform’s owners. Neither the EU nor the US has legislation in place governing post-mortem privacy.

The data is held by many organizations, the majority of which are unknown. Most are unwilling to delete these data. Öhman expresses concerns that profit-driven businesses may lack the reliability required to act as guardians of digital heritage. He questions whether governments or NGOs would be more suitable curators, but is unsure and cites George Orwell’s observation that ‘who controls the past, controls the future.’ However, these data could also prove beneficial to historians, policymakers, and society at large. Roy Rosenzweig observed that the data provide researchers with an ‘essentially complete historical record’.

The implications of all of this are significant and warrant further reflection. ‘The Afterlife of Data’ is an insightful book that offers a compelling perspective on the potential implications of data in the afterlife. It is highly recommended reading for those interested in this topic.

C. Öhman (2024). The Afterlife of Data: What Happens to Your Information When You Die and Why You Should Care, University of Chicago Press, 200 pp.

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De Nabije Toekomst van Archivering

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Nieuwe publicatie!

‘De toeslagenaffaire is nog niet voorbij of de malaise van de WIA buitelt eroverheen. De chaos bij de overheid groeit zo uit tot ongekende proporties waardoor de democratie verder onder druk komt te staan. Niet door het gewoonlijke en bijbehorende politiek gekonkel in de Tweede Kamer, maar omdat het ambtelijk apparaat zich naar binnen heeft gekeerd en het contact met burgers en bedrijven heeft verloren, uitzonderingen daargelaten.’

Dus worden de oude systemen en bestaande technieken gebruikt om de problemen aan te pakken. In een zoektocht naar oplossingen lijkt geld niet het probleem: er zijn miljarden uitgegeven zonder resultaat. Maar over de echte aanpak van de toeslagenaffaire is nooit goed nagedacht.

Welke feiten, middelen of regelingenwaren nodig voor effectieve actie? Waarom werden toeslagen eigenlijk stopgezet bij vermeende fraude? Is het probleem überhaupt oplosbaar? Handmatige informatieverzameling zonder bruikbaarheid voor gedupeerden, medewerkers, belangenbehartigers en machines om intelligent over regelingen heen te redeneren, leveren geen resultaten op. Technologieën van 20 jaar geleden, toen state-of-the-art, werken echt niet meer en alleen nieuwe middelen, nieuwe techniek inzetten, is het recept voor een volgende affaire.

Techniek zonder een goed concept en een heldere architectuur geeft slechts een illusie van hoop. De status quo hindert de oplossing omdat de communicatie al in de basis is verstoord. Het is onbeheersbaar complex geworden. De oplossing ligt in openheid, we moeten als overheid anders leren denken: transparant zijn en fouten erkennen.

Citatie: Geert-Jan van Bussel en Hans van Bommel (2024). ‘De nabije toekomst van archivering’, jrg, 78, november 2024, nr. 39, pp. 24-25.

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AI’s Power Demand

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AI results in a large increase of data center power demand, and does have a large effect on natural resources.

In 2018, OpenAI concluded that the computing power required to train a large AI model had doubled every 3.5 months from 2012 onwards. The accuracy of results and time efficiency that can be achieved by harnessing the computing power of a vast number of computers in data centres necessitates a considerable amount of electricity. A significant proportion of data centres globally continue to rely, to some extent, on fossil fuels, resulting in a notable surge in CO₂ emissions.

In 2020, researchers at the University of Massachusetts conducted an analysis of several natural language processing (NLP) models and determined that the energy expenditure associated with training a single model resulted in CO2 emissions of approximately 300,000 kg on average (equivalent to 125 return flights from New York to Beijing). The training of ChatGPT-3 has been found to require the consumption of 1.3 gigawatt hours of electricity, resulting in the generation of 550,000 kg of CO2. It is estimated by Bloomberg that the energy consumption necessary for training is only 40% of that required for operational purposes. Moreover, the training process necessitates the consumption of approximately 700,000 litres of water for the purpose of computer cooling. This quantity of water is equivalent to that which would be required by a nuclear power plant cooling tower.

In 2023, data centres operated by Google extracted a total of 24 billion litres of water from the environment. This represents a 14% increase compared to figures recorded in the previous year. In 2022, 20 billion litres of water were employed for the purpose of cooling. Two-thirds of this quantity was comprised of potable water.

Furthermore, data from Microsoft’s facilities indicate a 34% increase in cooling water consumption during the same period. In 2024, Microsoft’s CO₂ emissions were 30% higher than in 2020, while Google’s emissions increased by 48% over the past five years.

See this blogpost by GoldmanSachs

For literature used to compose this post, see here.

Publishd in a LinkedIn-post, november 2024.

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Co-Intelligence

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I have recently read Ethan Mollick’s book, Co-intelligence. Living and working with AI. The book is well written and an easy and enjoyable read. The author explores the potential of conversational AI (LLMs) in the context of personal and professional realms.

As the book makes clear, LLMs are adept at creative work and at identifying unanticipated correlations. It can falter in elementary intellectual pursuits. It is a statistical model of language that generates a plausible imitation of human responses by calculating the probability of clusters of words. The responses are devoid of cognitive processes, knowledge, or logical validation. They are the result of calculations regarding the optimal sequence of words. The provided response does not need to be accurate or true.

Mollick’s book presents four guiding principles for the practical and ethical use of LLMs:
[1] Use LLMs to investigate and delineate the boundaries of their functionalities.
[2] Engage in active monitoring of the responses provided, ensuring that they are accurate, true, and free from hallucinations.
[3] Treat conversational AI as a person. Critique its answers, clarify where necessary, reformulate questions, and offer feedback.
[4] Treat every LLM as if it is the least sophisticated model you ‘will ever use,’ recognizing that new iterations or models may offer improvements.

What struck me is the banality of conversational AI. It will be unknown whether the words were written by conscious minds or AIs and have ‘feeling’. Mollick refers to this as a ‘crisis of meaning’, but a crisis of language seems more accurate.

Mollick emphasizes the concept of ‘co-intelligence’, humans and AI working together to enhance productivity, creativity and decision-making. He proposes a symbiotic relationship in which AI tools enhance human capabilities. The book is full of anecdotes, examples, and texts that seek to demonstrate the veracity of that proposition. There is, however, no theoretical basis to support this assertion.

In contradiction, an article in Nature Human Behaviour in October 2024 found that, on average, human–AI combinations performed significantly worse than the best of humans or AI alone. See: M. Vaccaro, A. Almaatouq, and T. Malone (2024). ‘When combinations of humans and AI are useful: A systematic review and meta-analysis’, Nature Human Behaviour (2024). https://doi.org/10.1038/s41562-024-02024-1.

The book’s implications for the use of conversational AI in an educational context are interesting. This is in line with Mollick’s educational experience at the Wharton School of Business. He requires the use of such tools by his students. Allegedly, a teacher has the potential to significantly enhance the quality of learning outcomes.

The book’s lack of academic depth and its anecdotal nature make it an excellent introduction for those with limited knowledge of AI. I can recommend reading it, especially for students.

E. Mollick (2024). Co-Intelligence: Living and Working with AI, Penguin Portfolio, 256 pp.

Published as e LinkedIn post here.

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In de bureaulade

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Erna Havinga publiceerde onlangs een blogpost waarin ze ingaat op het verschijnsel dat bestuurders compliance issues negeren, zelfs als er auditrapporten liggen die wijzen op misstanden. Ze richt zich vooral op ‘informatiebeveiliging en privacy’.

Het verschijnsel is mij niet vreemd: ik weet dat vele advies- en auditrapporten over problemen met de informatiehuishouding in bureaulades gelegd worden en dat de aanbevelingen slechts marginaal worden opgevolgd. Redenen daarvoor zijn divers, maar een ervan is dat de noodzaak niet zo gevoeld wordt en dat het in ieder geval geen prioriteit heeft.

Die onachtzaamheid speelt al veel langer als het gaat om informatie, informatiemanagement en informatiebeveiliging. Er is genoeg wetenschappelijke literatuur beschikbaar waaruit blijkt dat door organisatie- en medewerkersgedrag vele mooie informatie(beleids)plannen onderuit worden gehaald. En daar doen managers en bestuurders aan mee. Ze hebben de mond vol over ‘informatie op orde’, maar ze kijken het liefst weg als het aan ‘de orde’ komt. Of, ook vaak toegepast, gaan weer een nieuw systeem implementeren dat ‘alle problemen oplost’. Het nemen van ingrijpende beslissingen kan daardoor weer worden uitgesteld en er hoeven ook geen ingrijpende maatregelen genomen te worden om voor ‘gewenst gedrag’ te zorgen.

Wat me wel van het hart moet: ‘hoezo privacy’? Daar gaat het toch helemaal niet over? De AVG spreekt over de ‘inbreuk op de vertrouwelijkheid, integriteit of beschikbaarheid van persoonsgegevens’. Het woord privacy komt helemaal niet voor in de AVG, en dat is niet zo vreemd omdat privacy niet iets is wat beschermd kan worden door een wet.

Privacy by design en Privacy Impact Analyses bestaan niet. Het zijn volgens de AVG ‘gegevensbescherming door ontwerp’ en ‘effectbeoordeling inzake gegevensbescherming’. Dat is iets heel anders en veel concreter dan ‘privacy’. Het gaat dus eigenlijk om informatiebeheer, -management en -beveiliging.

Informatie is iets waar menselijk gedrag en organisatiecultuur moeite mee hebben. Er wordt veel geroepen, maar weinig geregeld en ingegrepen. Hard roepen dat informatie belangrijk is, is makkelijker dan dat echt goed organiseren in een complexe omgeving.

Het is niet zo vreemd dat negatieve auditresultaten niet leiden tot actie. Ingrijpen om ‘gewenst gedrag’ te stimuleren is immers ‘moeilijk’, zeker in de informele organisatieculturen waar vele organisaties prat op gaan.

Voor gedrag in organisaties:

G.J. van Bussel, A Sound of Silence. Organizational Behaviour and Enterprise Information Management, Van Bussel Document Services, Helmond 2020.
Online bron: hier en hier.

E, Havinga (2024), ‘Rapporteren zonder resultaat; de frustratie van elke FG en CISO’. IB&P Blog. Online bron: hier.

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Harari’s Nexus

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I am not the first to comment on Yuval Harari’s new book Nexus. A Brief History of Information Networks from the Stone Age. The reception has been mixed. I was curious to see if Harari would offer more than ‘big ideas’ and ‘big history’ that doesn’t require archival sources and close reading before drawing conclusions. In Sapiens, Harari proposed big ideas with abandon, such as gossip as the basis of the ‘cognitive revolution’ and the agricultural revolution as the ‘biggest fraud’ in history. Both may be big ideas, but they are also untrue. His generalizations paint beautiful, well-written and easy to understand anecdotes, but most of them are just that: anecdotes, mostly unconfirmed, unproven and without context.

Nexus is a disappointing read. The book could have been shorter than the 528 pages Harari expects us to read. There is much that is interesting, even brilliant. But most are anecdotes without context or truisms presented as original ideas. Harari sets out to write about information, switches to writing about information media and, in the end, about artificial intelligence, which is neither.

But perhaps these stories and truisms are at the heart of the book? Early in the book, Harari debunks the ‘naive view of information’, the idea that gathering more information leads to truth, wisdom and power. According to Harari, there is no correlation between information, truth and power. Information networks are not about truth, they are about stories. Think of religions. They are successful because they have a large information network and they tell the same story. While his point about religions may be true, information networks are not just about stories. There are proven links between control of information, control of (imagined) truth and power.

The second half of the book is about computers and artificial intelligence, which Harari believes could lead to Armageddon. Most of his concerns seem reasonable, such as generative AIs poisoning online discussions. He becomes a prophet when he discusses how ‘AI overlords’ might create a new pandemic, a new kind of money, and a flood of fake news or incitement to riot on information networks. Fake news and incitement are already true, and we don’t need advanced ‘AI overlords’ for that. And a solution? Regulation and ‘institutions with strong self-correcting mechanisms’. Answering with a truism to fight the ‘end of days’? Such a solution will not prevent a situation in which, as he writes in the prologue, ‘we might find ourselves nowhere at all, as Earth becomes inhospitable for human life’. He could have done better.

Nexus lacks focus. Anecdotes from all over space and time are juxtaposed and accompanied by truisms, such as, at the end of chapter 11: ‘The only constant in history is change’. Which is true, of course, but I did not need Harari to know that.

Perhaps I am a naive information theorist. And maybe that is why I do not recommend this book for reading.

Y.N. Harari (2024). Nexus. A Brief History of Information Networks from the Stone Age, Random House, New York, 528 pp.

Published in a post on LinkedIn, September 2024.

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AI and digital preservation

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Interesting paper (february 2024) about the role of AI for the preservation of archives.

According to the abstract:
‘This paper delves into the historical evolution of preservation methods driven by technological advancements as, throughout history, libraries, archives, and museums have grappled with the challenge of preserving historical collections, while many of the traditional preservation methods are costly and involve a lot of manual (human) effort. AI being the catalyst for transformation could change this reality and perhaps redefine the process of preservation; thus, this paper explores the emerging trend of incorporating AI technology into preservation practices and provides predictions regarding the transformative role of Artificial Intelligence in preservation for the future. With that in mind, this paper addresses the following questions: could AI be what changes or creates a paradigm shift in how preservation is done?; and could it be the thing that will change the way history is safeguarded?’

I don’t really know if it would be a paradigm shift or that it changes the way archives are safeguarded.

That AI might redefine the process of preservation: I don’t doubt that. The process of preservation can be automated and the information needed to do so is available. So, it is quite possible that an Artificial Narrow Intelligence would be of enormous help in processing al those routine tasks now done by people.

But: it would mean indexing all known data about the process (processes) of preservation and meticulously designing an AI driven process, with several human controls.

Citation:
Z. Teel (2024). ‘Artificial Intelligence’s Role in Digitally Preserving Historic Archives’, Preservation, Digital Technology & Culture, Vol 53, NO. 1. Online source. For download here.

Published in a LinkedIn post in september 2024.

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Biologic Computer ?

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Luciano Floridi and Anna Nobre have published an interesting paper on SSRN about ‘conceptual borrowing’, the process by which a new discipline develops its technical vocabulary partly by ‘borrowing’ concepts from neighbouring disciplines, often giving the concept a new meaning.

The authors argue that through this extensive conceptual borrowing, the discipline of AI has ended up ‘anthropomorphizing’ computers, as computational brains with psychological properties. The brain and cognitive sciences themselves have developed a vocabulary that has led to descriptions of the brain and mind as if it were a computer that processes information, as if it were a ‘biological computer’.

This mixing of the jargon of these disciplines can lead to confusion and even harmful assumptions and consequences.

Artificial intelligence and human intelligence are far apart, but using the vocabulary of other, seemingly closely related disciplines can give a different idea. Clarity about concepts is really important, especially when using a concept in a new context.

The same happens with concepts from archival science being used in information and computer science with a completely different meaning. And vice versa! It leads to confusing discussions where people are talking about the same thing but completely misunderstand each other because of different meanings!

Interesting paper! Essential reading if you work with AI specialists!

L. Floridi, and A. Nobre (2024). Anthropomorphising machines and computerising minds: the crosswiring of languages between Artificial Intelligence and Brain & Cognitive Sciences (Centre for Digital Ethics (CEDE) Research Paper, No, forthcoming. Published on SSRN.

Published as a LinkedIn post here in september 2024.

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Two book reviews

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There are laying two new, just published books on my desk. I have already read the first one and am currently engaged with the second.

Both books are essential reading for anyone engaged in business, science or a professional field.

In his book, Technology is Dead. The Path to a More Human Future, Chris Colbert presents a framework for a more humane and human-centric relationship with technology, which, as we have created, we must now learn to manage. Central thesis of the book: technology has escaped human control with unintended and inevitable consequences.We have the choice to either continue pursuing technological advancement
at the expense of the planet, life, and our own humanity, or to assume responsibility and direct the course of technological development. We have no sufficient reason to unleash technology without consideration of the consequences.

The second book is Tiankai Feng‘s Humanizing Data Strategy. Leading Data with the Head and the Heart. It is important to recognize that people are emotional, irrational and impulsive, yet they also play a crucialrole in any data strategy. Nevertheless, integrating the human element into data strategy is a challenge. Tiankai presents a framework for understanding the human aspects of data strategy, linking human needs with business objectives through the concepts of competence, collaboration, communication, creativity, and conscience. By acknowledging the inherent biases and emotional influences in data analysis, it becomes evident that empathy, intuition and compassion are essential in the realm of data-driven decision-making. He presents a methodology for making data strategies more inclusive and impactful, combining personal anecdotes with professional knowledge. Fostering a collaborative and ethical data culture requires humanizing data practices, which is an essential step in developing effective and humanized data strategies.

The two books share a common theme of humanizing developments.

If this is a new trend, it is a welcome development.

Published in a post on LinkedIn in september 2024.

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Information governance and Small Language Models

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The advent of large, comprehensive AI models (such as OpenAI’s GPT-4) has had a profound impact on society, both beneficial and detrimental. The positive impact of AI includes stimulating innovation, generating audio, video, and text, and much more. However, the negative impact of AI includes environmental damage, data scraping that is mostly illegal, its immense costs, and it being the source of large amounts of misinformation, deep fakes, and so on.

There appears to be a tendency towards the adoption of smaller, more agile models that are specifically designed to efficiently perform a limited range of tasks. Such models are designed to perform these tasks at reduced cost in comparison to larger models. Such models offer advantages in terms of speed and can be deployed on local devices, thereby eliminating the need for constant cloud connectivity. This presents a substantial advantage for applications that require cost-effective, real-time processing and privacy.

Examples: Microsoft’s Phi models that, despite being 1/100th the size of GPT-4, can perform many tasks nearly as well. Or Apple’s initiative to run AI software entirely on phones using small models.

The reduction in data foorprint may prove to be a potential public relations asset.

The governance of information will be of significant importance in the utilization of these smaller models.

1. The functionality of small-scale AI models is contingent upon the quality of the data on which they rely. Consequently, these models require access to clean, accurate, and well-curated datasets to operate effectively. Information governance is a crucial aspect of this endeavour.

2. It is essential to guarantee that AI models adhere to regulatory standards and data privacy standards. Information governance frameworks assist organizations in navigating intricate regulatory terrains and implementing vital safeguards.

3. It is imperative that users and stakeholders have confidence in the impartiality, transparency, and fairness of AI models. The implementation of information governance practices, such as data lineage tracking and bias audits, facilitates the ethical design and use of those models.

What further arguments could be put forth to demonstrate the importance of information governance specialists within organizations?

Published in a post on LinkedIn in september 2024.

This post was a response on ‘Why Information Governance is Critical to the Success of Smaller, Nimbler AI Models’, published here.

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De arbeidsproductiviteit in Nederland

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De arbeidsproductiviteit in Nederland is weer lager. Los van het feit dat dat al lange tijd gaande is en het volstrekt onduidelijk is hoe dat cijfer uiteindelijk wordt bepaald (zelfs de gasprijs speelt een rol blijkbaar), gaat het feit dat er blijkbaar minder mensen inzetbaar zijn een rol spelen. Innovatieve technologie is dan een van de oplossingen, blijkbaar.

Innovatieve technologie is al jaren ingezet, zonder veel succes vaak. Dat lag niet aan die technologie, veelal aan de wijze waarop die technologie is geïmplementeerd en waarop er binnen organisaties mee wordt omgegaan. Zeker als het gaat om informatietechnologie gaan organisaties in implementatieprocessen tot het uiterste om die technologie volledig te doen lijken op de organisatie zoals die is. Technologie dient dan alleen maar om bestaande structuren te versterken, leidend tot een toename van bureaucratisering en verkokering. Niemand wordt er gelukkig van, maar we gaan er toch gewoon mee door. In veel gevallen wordt het ‘systeem’ achteraf ingevuld op basis van wat bij de koffieautomaat is afgesproken. Lach niet, want dat is in veel situaties nog steeds het geval.

Als je de arbeidsproductiviteit van je mensen wilt verhogen, denk dan aan:

Gebruik technologie zoals die gebruikt moet worden en gebruik AI daar waar daadwerkelijk verbeteringen kunnen worden aangebracht. Vergeet Generatieve AI, want die speelt hier geen rol van betekenis. Kijk naar kleinere, ‘slimme’ AI-toepassingen die bottlenecks in je werkorganisatie kunnen oplossen, waardoor mensen worden vrijgemaakt om ‘gewoon’ hun werk te doen.

Durf na te denken over je eigen organisatie, je eigen werkwijzen en je eigen ‘overhead’ in processen.

Durf je organisatie te veranderen zodat die gebruik kan maken van de mogelijkheden die de technologie biedt.

Durf je organisatiecultuur aan te pakken en maak het gedrag van jezelf en je mensen bespreekbaar. De meeste problemen met de toepassing van technologie zitten juist in die cultuur en in dat gedrag!

Durf te investeren in mensen. Vul vacatures op met mensen die wat ouder zijn, die een afstand hebben tot de arbeidsmarkt of die ondanks hun opleiding alles willen aannemen om nuttig te zijn. Ook onder hun ‘niveau!’ Als je een vacature hebt, en je stuurt mensen weg omdat ze te ‘oud’ zijn, geen ‘ervaring’ hebben in de sector, ‘overgekwalificeerd’ zijn of meer van dergelijke drogredenen, dan heb je geen tekort aan mensen.

En tot slot: houd op met de dooddoener dat het ‘wat langer duurt’ voordat je als klant geholpen wordt omdat het ‘te druk’ is!

Dat is die oude ‘cultuur’ en dat ‘oude’ gedrag waar je zo snel mogelijk vanaf moet!

Gepubliceerd in een post op LinkedIn, augustus 2024.

Deze post was een reactie op een nieuwsbericht op EW.

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Organizational structure and software design

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Melvin Conway’s Law, first posited in 1968, states that the architectural structure of a software system mirrors the organizational structure of the entity responsible for its development.

In the event that an organization comprises multiple teams engaged in a single project, the resulting software system will likely exhibit a structure that reflects the communication patterns between those teams. Even the largest software development companies, such as Microsoft and Google, are susceptible to the influence of this ‘law’.

Although typically discussed in the context of software, the observation is applicable more broadly to systems in general. Conway posits that social problems such as poverty, healthcare, and education reflect (or mirror) the structures of government organizations.

Let’s take this one step further.

The architectural structure of the software may reflect the structural configuration of the organization that designed it. Ultimately, however, the implementation of the software reflects the structural configuration of the organization that uses it, even if the structural configurations of the developer and the user are disparate (or even contradictory).

Could this be a potential explanation for organizational difficulties encountered when implementing information and records management software?

In addition to the more obvious explanations regarding organizational and employee behaviour, of course.

I was inspired by Nohan Nayak’s insightful post below, in which he also raises the question of whether AI agents are influenced by Conway’s law. This is a very interesting question, as it would have significant implications if they are.

Further reading:
Conway, M. E. (1968). ‘How do committees invent’, Datamation, Vol. 14, No. 4, 28-31.
Bindrees, M. A., Pooley, R. J., Ibrahim, I. S., & Bental, D. S. (2014). ‘How public organisational structures influence software development processes’, Journal of Computer Science, Vol. 10, No. 12, 2593.
Bailey, S. E., Godbole, S. S., Knutson, C. D., & Krein, J. L. (2013). ‘A decade of Conway’s Law: A literature review from 2003-2012’, 2013 3rd international workshop on replication in empirical software engineering research, IEEE, pp. 1-14.

Response to the following LinkedIn post:

Nohan Nayak, How Organizational Structure Impacts Software Design
This relationship, known as Conway’s law, states that “organizations which design systems … are constrained to produce designs which are copies of the communication structures of these organizations”.
There are several key ways in which organizational structure affects software design:

Communication and Coordination
Software components that need to communicate frequently tend to be developed by teams that interact closely. Conversely, loosely coupled teams will produce more modular software designs.

Specialization and Expertise
The division of labor and specialization of teams in an organization influences the modularity and layering of software systems. Teams focused on specific domains or technologies will produce components that are optimized for those areas but may not integrate as smoothly.

Decision-Making and Autonomy
The decision-making structure of an organization, whether centralized or distributed, impacts the flexibility and extensibility of software. Centralized decision-making, such as in a chief programmer team, can produce more consistent designs but may limit innovation. Decentralized structures like egoless or democratic teams allow more experimentation but may lack coherence.

Organizational Inertia and Legacy
Over time, an organization’s software tends to mirror the communication patterns and team structures that existed when it was originally built[1]. As an organization evolves, its software can become increasingly misaligned, requiring major refactoring efforts to untangle.

To mitigate the impact of organizational structure on software design, experts recommend:

– Aligning teams and components based on domain boundaries rather than technologies
– Fostering cross-team communication and coordination through shared goals, tools, and processes
– Empowering software architects to shape both the technical and organizational structures
– Regularly reviewing and iterating the organization’s structure to keep it in sync with evolving software needs

How does organisational structure impact software design? And will AI Agents behave the same way?

This intriguing illustration sheds light on the dramatised organisational structures of Amazon, Google, Facebook, Microsoft, Apple, and Oracle, perfectly echoing 𝗖𝗼𝗻𝘄𝗮𝘆’𝘀 𝗟𝗮𝘄: “𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝗱𝗲𝘀𝘁𝗶𝗻𝗲𝗱 𝘁𝗼 𝗯𝗲 𝗮 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝗶𝘁.”

This concept could become a critical hyperparameter in AI agent design, shaping their interactions and problem-solving approaches. Conway’s Law appears to be an emergent property of complex systems, but regardless, the fact that LLMs are trained on human decision-making makes it increasingly likely that Agents will emulate these same dynamics.

For the LinkedIn posting see here.

Publshed August 2024.

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