The analytical engine behind the argument is the concept of sociotechnical imaginaries, developed by science and technology studies scholars Sheila Jasanoff and Sang-Hyun Kim. These imaginaries are collectively held visions of desirable futures that a society pursues through science and technology, and they do more than reflect values; they actively shape which questions get asked and which get deferred. In East Asia, Son argues, the dominant imaginary has been one of catch-up and national ascent, a narrative in which technology functions primarily as the vehicle for escaping historical subordination and achieving prosperity. Within such an imaginary, ethical scrutiny of technology itself can appear not merely unnecessary but almost disloyal, a brake on the national project at precisely the moment acceleration matters most.

The first structural tendency Son names is importer’s pragmatism. For decades, East Asian economies consumed technologies designed, standardized, and philosophically pre-digested elsewhere. When the artifacts arrive ready-made, the ethical debates that accompanied their creation arrive, if at all, as afterthoughts or foreign imports. The philosophical labor of asking what a technology should be, rather than merely how well it works, was performed in other languages, other institutions, and other societies. Importing nations became extraordinarily skilled at adoption, refinement, and deployment, but the muscle of first-principles normative inquiry atrophied. The result is a policy culture fluent in technical specifications and market strategy but comparatively silent on the deeper questions of what these systems do to human relationships, labor, and dignity.

The second tendency, pursuer’s competitiveness, compounds the first. Nations that perceive themselves as chasing the technological frontier organize their entire innovation ecosystems around speed. Son points to South Korea’s recent drive to be among the world’s top three AI powers, including the selection of elite teams to build a sovereign AI model, as a contemporary expression of this logic. In a race, the metric is velocity, and ethical deliberation is structurally coded as friction. Frameworks such as the country’s new comprehensive AI law, enacted as the first of its kind, attempt to attach safety and trust requirements to development, but Son’s analysis suggests that legislation alone cannot counteract an imaginary in which national prestige is staked on being first. The competition of speed, he argues, must become a competition of direction.

The third tendency is the collectivist narrative of progress. Where technological advancement is framed as a collective national achievement, individual harms can be reframed as acceptable sacrifices on the road to shared glory. Son traces this pattern through a sobering catalogue of Korean disasters: the Seongsu Bridge collapse, the Sampoong Department Store collapse, and the Wenzhou train collision, alongside the Hwang Woo-suk stem cell fraud scandal. Each episode, in his reading, illustrates how the imperative of progress can suppress dissenting voices, marginalize whistleblowers, and delay accountability until catastrophe forces reckoning. Applied to AI, the same collectivist logic surfaces in policies like the rapid rollout of AI textbooks in schools, where documented confusion among students and teachers and concerns over digital overuse were subordinated to the national narrative of educational transformation.

The fourth tendency is perhaps the most culturally distinctive: the limited centrality of what Son calls the Frankenstein complex. In Western societies, Mary Shelley’s creature and its descendants have seeded a deep cultural anxiety about creations that turn on their creators, an anxiety that fuels public resistance, precautionary regulation, and a rich literary and philosophical tradition of technological self-doubt. Thinkers from Jacques Ellul to Hans Jonas built ethics on this wariness. East Asian societies, by contrast, have largely absorbed technology without an equivalent mythic register of rebellion and retribution. The absence of this cultural brake has eased acceptance and accelerated diffusion, but it has also removed a natural trigger for public deliberation. When nothing in the collective imagination insists that new powers demand new humility, normative debate must be built deliberately rather than inherited.

Son’s case study of Korean AI adoption shows how these four tendencies converge in the present. The country has positioned itself as an attractive test bed for global AI companies, a role that brings investment and visibility but also imports the industry’s well-documented externalities, from the exploitation of low-paid data workers abroad to embedded gender bias in generative systems. Domestic civil society groups, such as the Institute for Digital Rights, have produced reports on AI’s human rights implications, yet the policy mainstream remains oriented toward development targets. Even labor conflict carries the pattern: when Hyundai’s union battled the rollout of Atlas robots, the dispute was framed primarily as an industrial relations problem rather than an occasion for society-wide reflection on automation’s direction. The structural tendencies do not prevent ethical debate, but they consistently narrow its scope and blunt its force.

The paper’s constructive proposal is deliberately not a call for culturally distinctive principles. Son explicitly declines to argue that Confucian benevolence or Buddhist non-attachment should be grafted onto AI governance as a regional signature. Such an approach, he suggests, misidentifies the problem as one of content when it is actually one of structure. Instead, he outlines a fourfold redirection of East Asian techno-modernity: from passive importer to responsible developer, meaning that nations must now own the full normative consequences of the systems they build and deploy; from competition of speed to competition of direction, rewarding societies that choose better trajectories rather than merely faster ones; from national collectivism to expanded responsibility, extending the circle of moral concern beyond the nation to workers, users, and communities elsewhere who bear the costs of AI supply chains; and from faith in progress to strategies of survival and human flourishing, a shift in the underlying imaginary itself.

The implications reach well beyond Korea. As AI development globalizes, the assumption that ethical frameworks can simply be exported from the societies that built the technology is increasingly untenable, and the temptation to answer with a menu of regional principles grows. Son’s analysis offers a sharper diagnostic: the question is not which tradition supplies the right maxims, but which structures allow any maxims to matter. A society whose imaginaries celebrate acceleration, whose institutions reward catching up, and whose culture lacks inherited scripts of technological suspicion will neutralize even the most elegant ethical principles. Conversely, restructuring those imaginaries, through democratized technology governance of the kind theorists like Langdon Winner and Andrew Feenberg have long advocated, could make deliberation constitutive rather than ornamental.

What makes the argument resonate now is the timing. East Asia is no longer merely importing AI; Korea has enacted the world’s first comprehensive AI safety law, launched national committees to claim a place among the top three AI powers, and embedded AI into classrooms. The region has crossed the threshold from pursuer to potential leader, and with that transition comes the loss of the importer’s alibi. If Son is right, the next chapter of East Asian AI will be judged not by how faithfully it applies ancient wisdom to modern machines, but by whether societies forged in the crucible of catch-up development can invent a new imaginary, one in which asking where technology should go is as prestigious as asking how fast it can get there.

Subject of Research: Structural analysis of East Asian AI ethics and techno-modernity using South Korea as a case study

Article Title: Do we need an East Asian AI ethics?: South Korea as a paradigmatic case

Article References: Son, W.-C. (2026). Do we need an East Asian AI ethics?: South Korea as a paradigmatic case. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03336-8

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03336-8

Keywords: AI ethics, East Asia, South Korea, techno-modernity, sociotechnical imaginaries, Confucianism, Frankenstein complex, AI governance, AI policy, philosophy of technology, national competitiveness, responsible innovation

Vučko’s argument begins with a simple observation about the structure of human experience. Nearly everything people do, from careers to hobbies to relationships, involves investing psychological or physical resources toward outcomes that are not guaranteed in advance. A surgeon operates without knowing exactly how the patient will recover. A novelist writes without knowing whether the book will find readers. A climber ascends a wall that might defeat them. These two features—effort and uncertainty—are usually treated as costs to be minimized. Technological progress, in its idealized form, promises precisely that: machines that work harder than we do and predict outcomes better than we can. But Vučko asks what remains of an activity once both parameters are optimized away.

To answer that question, he grounds his analysis in self-determination theory, one of the most influential frameworks in contemporary motivation science. Developed by psychologists Edward Deci and Richard Ryan, the theory holds that human well-being depends on satisfying three basic psychological needs: autonomy, competence, and relatedness. Crucially, the sense of competence is not fed by easy successes. It grows from challenges that demand genuine investment and from outcomes that could plausibly have gone otherwise. When an activity requires no effort and carries no risk of failure, the psychological machinery that generates satisfaction from achievement has nothing to work on. Vučko contends that effort and uncertainty are therefore not incidental annoyances but prerequisites for any sufficiently meaningful activity.

The thought experiment at the heart of the paper imagines extreme automation taken to its logical endpoint. Suppose technology advanced to the point where machines outperform humans at every task—diagnosing disease, writing legal briefs, composing music, driving vehicles, even conducting scientific discovery. There are already signs of this trajectory. GPT-4 has been shown to pass the bar exam, and AI systems for breast cancer screening have matched or exceeded human radiologists in international evaluations. Moravec’s paradox, the observation that tasks easy for humans remain hard for machines even as machines conquer tasks humans find difficult, is steadily eroding as artificial intelligence encroaches on domains once considered exclusively human.

What happens to such activities in a fully automated world? Vučko’s answer is that they do not disappear, but they change character. Activities in which humans can no longer compete with technology become, in his terms, recreation. A chess player today already inhabits this condition: since Deep Blue defeated Garry Kasparov in 1997, no human seriously claims to play chess better than a machine, yet millions still play. The game has become something played for enjoyment rather than objective achievement. Vučko argues that this transformation is sufficient to conditionally erode the subjective meaningfulness of activities for some individuals. Knowing that your best effort is objectively trivial compared with what a machine can do changes the texture of the experience, even if the pleasure remains.

The erosion does not stop with obviously competitive pursuits. Vučko emphasizes that no activity exists in isolation. Even autotelic activities—those done for their own sake, such as socializing, self-reflection, or reading fiction for the pleasure of emotional transportation—can be negatively affected by the awareness of one’s diminished capacity for objective contribution. A person might still enjoy a long walk in the mountains, but if they know that no physical accomplishment of theirs carries any weight in a world where machines handle everything, the background sense of being a capable, contributing agent begins to thin. Meaning, on this account, is not generated activity by activity in a vacuum; it depends on a broader self-understanding in which one’s efforts matter.

This leads to what may be the paper’s most striking implication: there is a limit to how much human distress technology can alleviate without producing a new kind of suffering. Much of the motivation for automation is the reduction of toil, risk, and anxiety. But if Vučko is right, the sources of distress he targets—uncertainty about outcomes and the strain of effort—are the same sources from which meaningful activity draws its nourishment. Eliminating all sources of distress also eliminates the potential for meaningful activities. The utopian project, pursued without limit, becomes self-defeating. Limited automation, by contrast, is conducive to meaningful lives: it removes drudgery while leaving humans room to strive, fail, and achieve on their own terms.

The argument engages a rich philosophical tradition. Robert Nozick’s famous experience machine thought experiment anticipated the intuition that pleasurable experiences divorced from genuine achievement feel hollow. Albert Borgmann’s philosophy of technology distinguished between the shallow comforts of the device paradigm and the deeper satisfactions of focal practices that demand engagement. More recently, John Danaher has explored whether life in a world without work can be worth living, and L. Scripter has defended the possibility of meaningful lives alongside artificial intelligence. Vučko’s contribution is to identify a specific mechanism—the joint minimization of effort and uncertainty—and to trace its consequences systematically, rather than relying on general appeals about the dignity of labor.

Notably, the paper does not claim that a highly automated future must be bleak. Moderate automation, which relieves people of tedious and dangerous work while preserving domains of genuine human challenge, could expand the space for meaningful activity rather than contract it. The concern is specifically with unlimited automation—the endpoint at which no domain of human striving retains objective significance. Vučko also acknowledges an important boundary condition: transhumanist scenarios in which humans merge with technology warrant separate consideration. If the striving agent is itself augmented, or partially constituted by the machines, the analysis of who is investing effort and bearing uncertainty changes fundamentally, and the existential calculus may differ.

For readers watching generative AI reshape creative and professional landscapes, the analysis offers a framework for anxieties that are often dismissed as mere nostalgia or Luddism. Creative displacement anxiety—the unease felt by artists and writers whose skills seem suddenly replicable—may reflect a genuine psychological injury, not simple economic fear. If competence, effort, and uncertainty are pillars of meaning, then watching machines master one’s craft after years of dedicated practice strikes at something deeper than income. Vučko’s paper suggests that societies racing toward maximal automation should ask not only what tasks can be automated, but what human capacities for meaning they are quietly dismantling—and whether some friction is worth keeping.

Subject of Research: The impact of extreme technological automation on effort, uncertainty, and the meaningfulness of human activities

Article Title: The limits of technological progress: effort, uncertainty, and meaningful activities

Article References: Vučko, D. (2026). The limits of technological progress: effort, uncertainty, and meaningful activities. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03356-4

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03356-4

Keywords: automation, technological progress, meaning in life, self-determination theory, artificial intelligence, human obsolescence, effort, uncertainty, recreation, philosophy of technology, well-being, transhumanism

Anders was writing at a moment of vertiginous technological change, when the first electronic computers were being unveiled to a public still reeling from the atomic bomb. He saw that people standing before these machines experienced an unfamiliar mixture of awe and self-doubt. Humans, he observed, are inferior to their technological appliances in strength, speed and precision, in much the same way that human thinking fares badly against the thinking power of computing machines. T’s response to this realisation was not simply intellectual. It registered in his body, in a sense of standing clumsily and imprecisely under the gaze of perfect devices. Anders described the shame as being about having naturally grown rather than having been made, about owing one’s existence to the blind, uncalculated, archaic process of procreation and birth, in stark contrast to immaculate products that are carefully designed through and through.

The concept remained obscure for decades. The provocative essay in which Anders identified Promethean shame was first translated into English only in 2016, and a full translation of the book in which it appeared, The Obsolescence of the Human, has only just been published by University of Minnesota Press. Sparrow argues this delay is unfortunate, because Promethean shame is all too evident in contemporary social, political and philosophical responses to AI. If the room-sized computers of the 1940s were enough to provoke the emotion in a few sensitive observers, the intelligent machines of today evoke it nearly universally, and the cultural consequences are everywhere to be seen.

Consider the rhetoric that now surrounds artificial intelligence. Breathless claims about superhuman performance are a staple of both popular and academic writing on almost any topic. We are constantly told we must get ready for AI, that we must dedicate ourselves to understanding it, and that it is inevitable humans will have to make way for AI and robots as they take our jobs. Leading philosophers insist there is a not-insignificant chance AI will become thousands of times smarter than us, that we might outsource our conscience to machines, and that we should live in fear of being turned into paperclips. Prominent scientists argue the future belongs to machines and that our only recourse is to leave our bodies behind as mind uploads in virtual worlds, or as digital simulations derived from our social media profiles. Sparrow notes that Marvin Minsky’s famous quip about future computers, that if we are lucky they might decide to keep us as pets, captures the vibe of contemporary AI discussion and stands as pithy an expression of Promethean shame as anything Anders himself coined.

But Anders’s analysis goes deeper than rhetorical excess. He argued that Promethean shame expresses a fundamental change in our sense of what it means to be human. New technologies establish new tasks for us, tasks for which our evolved bodies and minds are poorly suited. A pilot landing a jet must make complex decisions in split seconds. A student who wants to use AI to review the philosophical literature responsibly must, in order to check the results, possess a knowledge of that literature which by hypothesis they lack, and which may in fact be impossible to acquire. In this new world, humans in their natural state appear as faulty constructions when measured against the tasks they face. Yet, as Anders pointedly observed, humans can only be considered constructions, faulty ones even, once they are classed as machines. The comparison itself smuggles in a dehumanising premise.

Sparrow finds this logic at work in the way computational metaphors have colonised human self-understanding. Machine intelligence must be possible, we are told, because we are just machines made of meat. He detects it too in a subtle rhetorical manoeuvre: complex, multidimensional human activities are redescribed in narrow, quantifiable terms that machines can perform, and then we are told the machines do it better than we do. And he perceives it in proposals to hand over roles once considered quintessentially human, requiring touch and physical comfort, to machines, whether as sexual partners, digital friends, or caregivers for the elderly. Each case involves quietly shrinking the definition of a human task until a machine can be declared its superior.

Perhaps the most striking part of Anders’s argument concerns freedom. He noticed that the world of fabricated products presents itself as indefinite, open and malleable, daily intent on reforming itself and adapting to new situations, while the human body remains what it always was, still yesterday’s body, still the body of our parents and ancestors, indistinguishable from that of a troglodyte. Seen from the perspective of machines, the body looks conservative, anti-progressive, obsolete and unchangeable, a dead weight in the rise of machines. The result is a strange inversion: the machine’s potentials are nurtured, giving it a free hand becomes a duty, and stifling its potential becomes an immoral deed. The machine presents itself as the subject of desire and demand, expecting humans to exert themselves to come up with ever better offers. Anders summarised the inversion bluntly: things are free, and human beings are unfree.

This inversion, Sparrow suggests, is even more evident today than when Anders was writing. Where people once believed in the progressive force of human reason, it is now widely believed that technology makes revolutions, not people. The future, we are told, belongs to spiritual machines, with humans along for the ride if we are included at all. Our most urgent task, on this view, becomes ensuring that the superintelligences destined to control humanity’s fate will be friendly to us. Anders also linked Promethean shame to what he called human engineering, the project we now call transhumanism. Humans, he wrote, are deserting to the camp of their machines, having convinced themselves that machines are ontologically superior to beings who are born. They want to make themselves because they no longer want to be something not-made, feeling indignant not that they were made by others but that they were not made at all. Sparrow observes that one would be hard pressed to find a better description of today’s tech billionaires and their transhumanist enablers, with their obsession with designer babies, robots, mind uploading and immortality. In Anders’s arch formulation, the experiments of human engineering are the initiation rites of the robotic age.

There is a final, darker twist. Like shame more generally, the lack of self-confidence associated with Promethean shame leads us to behave in ways that make things worse. The more miserable humans become in trying to keep pace with their creations, the more frantically they multiply the bureaucracy of machines, sub-machines and sub-sub-machines, which in turn deepens their misery. What appears to be liberation from the tyranny of nature may result in an unprecedented subjugation to technology, as Oliver Rachello has suggested in drawing lessons from Anders for AI ethics. Anders himself put it starkly: humans turned themselves into masters only to find a new way to become slaves.

Escaping this trap, Sparrow concludes, will require serious cultural self-work, some of which he hopes will take place in the pages of AI & Society. The remedy he proposes is disarmingly simple in principle. We are not machines, and we need feel no shame when lifeless things perform exactly as we have designed them to perform. That we are born rather than made is, as Hannah Arendt argued, a condition of our freedom and our agency, not a defect to be regretted. The urgent task of building a world in which our creations serve us, rather than us serving them, begins with recognising and overcoming Promethean shame. Seventy-five years after T stood blushing before a wall of vacuum tubes, the emotion he felt has become the unacknowledged background hum of the AI age, and naming it may be the first step toward thinking clearly about the machines we have made.

Subject of Research: Promethean shame and the philosophical response to artificial intelligence

Article Title: AI and Promethean shame

Article References: Sparrow, R. (2026). AI and Promethean shame. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03371-5

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03371-5

Keywords: artificial intelligence, Promethean shame, Gunther Anders, philosophy of technology, transhumanism, AI & Society, Robert Sparrow, human obsolescence, machine intelligence, mind uploading, AI ethics, human dignity

Sudarsono names the condition operational opacity: the absence of any visible distinction, at the point of interaction, between AI outputs produced through probabilistic generation and those produced through reliable computation. When a chatbot calculates a sum, it may be running a genuine computational process, or it may be generating statistically plausible text that resembles a calculation. To the user staring at the interface, the two are indistinguishable. Both arrive in the same clean, authoritative prose. Both carry the same tone of competence. The difference between a verified computation and a sophisticated guess is structurally invisible, and that invisibility, the paper argues, is not a cosmetic flaw but a defining epistemic hazard of the AI era.

The framework is careful to distinguish operational opacity from two concepts already well established in the literature. The first is algorithmic opacity, the familiar problem that the internal workings of machine learning systems are inscrutable even to their designers. Sudarsono relocates the problem: operational opacity lives not inside the architecture but at the moment of interaction itself, on the surface where human meets machine. A perfectly explainable system could still be operationally opaque if its interface fails to signal which mode of processing produced a given answer. The second distinction is from automation bias, the documented human tendency to over-trust automated systems, studied extensively since Parasuraman and Manzey’s influential 2010 synthesis in Human Factors. Automation bias describes a failure of evaluation, a user accepting an output too readily. Operational opacity names a condition that precedes evaluation: the user cannot even begin to judge an output properly because nothing tells them what kind of process generated it.

What fills that vacuum, according to the paper, is a culturally embedded assumption Sudarsono calls the omnicompetence assumption: the belief, absorbed from decades of computing culture, that a machine labeled intelligent is competent across all domains. A calculator computes; a search engine retrieves; but a system that converses fluently about history, law, medicine, and arithmetic seems, to the untrained eye, to master everything at once. When this assumption meets an uninformative interface, the result is modality collapse, the inability to distinguish what kind of process generated a given response. And the downstream consequence of modality collapse is what the paper terms capability illusion: users systematically overestimate what the system is actually doing reliably, because the surface presentation of a guess and the surface presentation of a computation are identical.

The empirical backdrop for these concerns is growing. Survey research published in the Journal of Medical Internet Research in 2023 by Choudhury and Shamszare examined how user trust shapes the adoption and use of ChatGPT, and a 2026 study by Zhang, Tian, and Deng in Interacting with Computers explored the dimensionality and structure of user trust in the same system. Related work by Steyvers and colleagues, posted as a preprint in 2026, probes what the authors call AI-mediated metacognitive decoupling, suggesting that the usual calibration between confidence and competence may break down in distinctive ways when judgment is outsourced to AI. Trust, in other words, is flowing toward systems whose operational modes users cannot inspect, and the trust literature is only beginning to map what that asymmetry does to human reasoning.

Against this backdrop, Sudarsono constructs the Dual-Mode Awareness Framework, or DMAF, built around three analytically independent dimensions. The first is modality perceptibility: can the user perceive, from the interaction alone, whether a response was probabilistically generated or reliably computed? The second is verification effort: how much work does it take to check the output independently? Verifying a chatbot’s summary of a historical event might require minutes of archival digging; verifying its arithmetic requires a calculator and seconds. The third is stakes sensitivity: what happens if the answer is wrong? A hallucinated recipe costs a dinner; a hallucinated drug dosage or a miscomputed financial figure can cost far more. The framework’s central analytical claim is that epistemic risk is determined by the interaction of these three dimensions rather than by any one of them in isolation. A low-stakes exchange with high verification ease may tolerate near-total modality collapse; a high-stakes exchange with difficult verification and imperceptible modality is where the framework locates the greatest danger.

The normative backbone of the framework draws on two philosophical traditions chosen, as the paper explains, because each names a feature of AI-mediated exchange directly rather than by analogy. From Bernard Stiegler’s pharmacological account of technology, developed across works including Technics and Time and elaborated with Antoinette Rouvroy in their 2016 analysis of the digital regime of truth, DMAF takes the view that technology is always both poison and remedy: the same systems that erode epistemic independence could be redesigned to support it. From Jürgen Habermas’s theory of communicative action, first published in English translation in 1984, the framework borrows an ethics of communicative transparency, the idea that participants in genuine communication must be able to understand the conditions under which claims are being made. An interface that hides whether a claim is computed or generated, on this view, violates a basic precondition of rational exchange.

Notably, DMAF does not prescribe interface redesign. Sudarsono is explicit that the framework operates at the level of user awareness, offering a diagnosis rather than an engineering specification. But the diagnosis carries a pointed distributional claim: primary responsibility for operational opacity falls on those who design and deploy AI systems, not on the users who encounter them. The argument is that users cannot be expected to detect a distinction that the interaction surface is structurally engineered, or at least structurally neglected, into invisibility. At the same time, the paper shows why this responsibility distribution does not require the framework to dictate exactly how designers should discharge their obligation, preserving room for plural solutions ranging from labeling conventions to confidence signaling to mode indicators.

The article situates itself within a broader lineage of human-centered computing thought. It reaches back to Card, Moran, and Newell’s foundational 1983 work on the psychology of human-computer interaction, and engages Ben Shneiderman’s 2022 manifesto Human-Centered AI, which insists that automated and human-controlled modes should be designed to complement one another. It also dialogues with Tim Miller’s 2019 survey of explanation in artificial intelligence, the Partnership on AI’s 2021 workstream report on explainable AI in practice, and Langdon Winner’s classic 1980 question of whether artifacts have politics. By connecting these strands, Sudarsono positions operational opacity as a gap that the explainability movement, focused as it is on opening the black box, has largely overlooked: even a fully explained system can still fail to tell you, in the moment, whether today’s answer was computed or merely composed.

The stakes of the argument extend beyond interface design into what the paper’s subtitle calls epistemic sovereignty, the capacity of individuals and societies to govern the conditions under which they know things. If fluency is mistaken for computation at scale, in classrooms, newsrooms, clinics, and finance departments, the collective ability to distinguish verified knowledge from plausible generation erodes. DMAF’s contribution is to give that erosion a name, a structure, and a locus of responsibility. Whether designers respond with visible mode indicators, verification affordances, or entirely new interaction conventions remains an open question, but the framework makes one thing clear: the problem is not that users are careless with AI. It is that the interface, as currently constituted, gives them nothing careful to be careless with.

Subject of Research: A conceptual framework for operational opacity and epistemic risk in human-AI interaction

Article Title: The dual-mode awareness framework (DMAF): toward epistemic sovereignty in AI-mediated knowledge societies

Article References: Sudarsono, P. (2026). The dual-mode awareness framework (DMAF): toward epistemic sovereignty in AI-mediated knowledge societies. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03376-0

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03376-0

Keywords: operational opacity, Dual-Mode Awareness Framework, human-AI interaction, epistemic sovereignty, large language models, modality collapse, capability illusion, automation bias, philosophy of technology, explainable AI, epistemic responsibility, AI trust