One of the most striking threads in the collection concerns the way human expertise quietly moderates algorithmic decisions in high-stakes environments. Carboni and colleagues examine clinical settings where violence is a real possibility, showing how nurses intervene in and influence algorithmic outputs. The researchers highlight the importance of the doubt that nurses introduce into the decision-making process, treating this cultivated skepticism not as friction to be eliminated but as a vital contribution to collective judgment. In a related vein, Van Voorst documents what is called collaborative tinkering between humans and algorithms in the context of public health, arriving at conclusions that largely echo the clinical analysis. Both studies suggest that the fantasy of fully automated decision-making dissolves under close empirical scrutiny, replaced by a messier but more accurate account of humans and machines reasoning together.

The volume also confronts the hidden human labor on which algorithms depend. Cabitza and colleagues coin the neologism cybork, a fusion of cyborg and work, to emphasize that algorithms frequently rest on human effort. Bennett and colleagues explore the social and power dynamics that arise when humans clean data so that algorithms can operate more efficiently and more correctly. Their argument, aligned with Cabitza and colleagues, is that this labor should not be occluded as if the algorithms were completely autonomous. Instead, algorithmic processing should be understood as a form of collective networking and human-machine collaboration. This reframing carries real political weight: when the workers who make machine learning possible remain invisible, they also remain uncredited, unprotected, and excluded from conversations about how the resulting systems should be governed.

Entanglement, however, runs in both directions. Several essays trace the recursive feedback loops through which algorithms influence human behavior at the same time that humans shape algorithms. Lynch and colleagues demonstrate this dynamic at a microscale, examining how the introduction of robot tour guides into two Nevada museums helps to create the distinctive social spaces that visitors encounter. The robots are not merely props; they actively reorganize how people move, gather, and interact. Kalthoff and Link extend the analysis to the fabrication of humanoid robots designed to create social dynamics through their interactions with humans, citing the pioneering Kismet robot built by Cynthia Breazeal, an early attempt to engineer machines that could engage humans on an emotional register.

At the largest scale, Richard Groß argues that Large Language Models, through their stochastic procedures, are shaping social reality in their own image. A parallel argument has been advanced by Louise Amoore and her colleagues, who focus on the principal technical characteristics of LLMs, such as latency and tokenization, and contend that these systems create a world model that is rapidly propagating through the infrastructures of developed societies. The claim is technically grounded rather than metaphorical. Because LLMs mediate communication, search, and increasingly administrative processes, the statistical regularities embedded in their training and generation procedures can become the regularities of everyday social life, closing a loop in which human output trains the machines that then shape human output.

Beyond empirical case studies, the collection presses for a change in the frameworks, metaphors, and narratives that construct AI-human relations. Harry Halpin urges readers to think in terms of collective intelligence rather than artificial intelligence, a shift that relocates smartness from a single machine to a network of people and tools. Lewis and colleagues point toward indigenous knowledges as demonstrating what they call abundant intelligences that extend throughout the natural world. Hayles singles out this lesson as a general template for how communities can assume control over the kinds of AI that get created, the purposes those systems are designed to fulfill, and the human interests they can support and help to enact. The implication is that the trajectory of AI is not fixed by the technology itself but by the social arrangements that surround it.

Other contributors extend this critical reframing in complementary directions. Bennett and colleagues call for Responsible Artificial Intelligence, in which alignment with human values is treated as the primary design criterion rather than an afterthought. Bory and colleagues look critically at AI narratives themselves, arguing that visions with more modest ambitions, the so-called weak rather than strong versions of AI, hold more potential to lead to futures beneficial to humans. Nugent critiques the pervasive analogy between biological and technological evolution, showing that the analogy works only by suppressing crucial differences that reveal the complexities of biological processes. Together these essays suggest that the stories we tell about AI are not decoration; they actively determine which futures seem possible and which are dismissed before they are tried.

Perhaps the most technically provocative contribution comes from Fabian Offert and colleagues, whose essay on protein folding as predicted by AlphaFold and other Transformer technologies deconstructs the widespread belief that LLMs produce natural language. As the authors show, through tokenization, word embedding, and positional embedding, the linguistic qualities of language are taken apart, reconstructed as numbers in a high-dimensional vector space, and then reconstituted through stochastic procedures that differ radically from how natural language is used and understood by humans. What emerges is not language in any human sense but a new episteme, one that creates and reinforces an intuitive knowledge of sequence. That intuition about sequential structure, they suggest, is precisely what makes these architectures so powerful for predicting how chains of amino acids fold into functional proteins, a problem that had resisted solution for half a century.

The significance of this analysis extends well beyond molecular biology. Hayles notes that it resonates with her own criticism of the naturalness of machine language, and she argues that analyses like those of Offert and colleagues open new possibilities for literary criticism, cultural critique, and critical code studies. If the outputs of LLMs are the products of vector-space arithmetic and probabilistic sampling rather than meaning in the human sense, then humanists have a crucial role in explaining what these systems actually do, what they cannot do, and where their apparent fluency misleads the people who rely on it. The special issue thus positions the humanities not as a skeptical bystander to the AI revolution but as an essential participant in its technical and conceptual development.

The overall message of the collection is both a warning and an invitation. Hayles concludes that readers will find a plethora of diverse approaches, specific demonstrations, and game-changing ideas in these essays, all pointing to the ongoing necessity for critique and analysis by humanists, cultural critics, and others as powerful technologies develop at breakneck speed. We cannot afford to ignore AI, she writes, and the essays help chart paths that allow us to understand these systems, participate in their development, and have a say in how they are and will be used. In an era when headlines oscillate between utopian hype and existential dread, the volume offers a third way: careful, empirical attention to the actual entanglements of humans and machines, and a insistence that the shape of that entanglement remains a collective human choice.

Subject of Research: Human-AI entanglement and the mutual shaping of humans and algorithms in society

Article Title: Entangling humans and AI

Article References: Hayles, N. K. (2026). Entangling humans and AI. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03387-x

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03387-x

Keywords: artificial intelligence, human-AI collaboration, large language models, collective intelligence, algorithmic decision-making, data labor, responsible AI, protein folding, AlphaFold, AI narratives, posthumanism, AI & Society

The core of the framework is a tri-lens analytical matrix that crosses three dimensions. The first dimension consists of three moral traditions: utilitarian ethics, which weighs aggregate outcomes and costs; deontological ethics, which focuses on duties, rights, and rules that must not be violated regardless of consequences; and virtue ethics, which asks what a good practitioner with good character would do in a given situation. The second dimension distinguishes AI’s two operational arenas. Frontstage systems are those clients directly encounter—chatbots, screening tools that shape decisions about their families, monitoring devices in their homes. Backstage systems are the algorithmic administration that clients rarely see: automated eligibility checks, data-matching pipelines, and risk-scoring infrastructures that quietly sort populations. The third dimension consists of four constitutive social-work values: privacy, self-determination, social justice, and human dignity. Every AI deployment in social welfare, the paper contends, can be located within this twenty-four-cell matrix, and each cell generates distinct ethical questions that generic AI-principle frameworks tend to blur.

This specificity matters because social work is not a generic domain for AI ethics. The discipline is anchored in relational practice—the idea that change happens through sustained, trusting relationships between practitioners and the people they serve—alongside care ethics and anti-oppressive praxis. A framework borrowed wholesale from medicine or finance risks missing what is distinctive about welfare encounters: they involve people in situations of dependency, often coerced or involuntary, where the state itself is both the service provider and the surveillance authority. Hwang argues that existing scholarship documents many harms of welfare AI—bias, opacity, surveillance, and the erosion of professional discretion—but remains conceptually fragmented, producing case-by-case critiques without a shared diagnostic language. The matrix is an attempt to supply that language, and to make explicit that different AI modalities pose different risks requiring different safeguards.

To demonstrate the framework’s reach, the paper applies it to three purposively selected cases whose heterogeneity spans continents and technologies. The first is the Allegheny Family Screening Tool in the United States, a predictive risk model used in child welfare screening in Allegheny County, Pennsylvania. The second cluster concerns algorithmic welfare administration: the Dutch SyRI system, which mined government data to detect suspected welfare fraud until a court curtailed it; the Australian Robodebt scheme, which automated debt recovery against benefit recipients and became one of the country’s worst public-administration scandals; and South Korean crisis-household detection, which uses administrative data to identify families at risk. The third case examines digital-care technologies in Korea and OECD comparator countries, where sensors, wearables, and remote-monitoring platforms mediate care for older adults. Together these cases show that a child-welfare risk score, a fraud-detection pipeline, and a care robot are not ethically interchangeable, even though all are labeled AI.

The frontstage–backstage distinction does much of the analytical work here. A frontstage tool like the Allegheny screening model directly shapes what a caseworker sees and does at the moment of first contact with a family. Its errors are consequential and intimate: a falsely high risk score can trigger intrusive investigation, while a falsely low one can leave a child unprotected. Because the tool operates at the point of professional judgment, the framework treats it as ethically permissible only if it augments that judgment—informing the worker’s assessment without displacing relational authority. A backstage system like SyRI or Robodebt, by contrast, operates on populations at scale, often without individual knowledge or meaningful consent. Its harms are structural: wrongful debt notices, discriminatory targeting, and the conversion of welfare applicants into suspects. The framework suggests that backstage automation faces a higher ethical bar precisely because no relational check exists to catch its errors before they land on people’s lives.

The three moral traditions also pull in different directions, and the matrix makes those tensions visible rather than hiding them. A utilitarian assessment of a predictive screening tool might emphasize aggregate benefits: earlier identification of at-risk children, more efficient allocation of scarce caseworker time, and prevention of tragedies. A deontological assessment asks whether the system respects the rights of the people it scores—whether they know they are being scored, whether they can contest the score, and whether consent is meaningful at all. A virtue-ethical assessment asks what the technology does to practitioners: does it cultivate attentiveness, humility, and care, or does it train workers to defer to a number and abandon the interpretive habits that relational practice requires? The paper’s position is that an AI system must survive scrutiny from all three lenses, not merely the consequentialist one, because welfare systems have historically justified harms to individuals by appeals to aggregate efficiency.

The four social-work values function as the normative anchors of the analysis. Privacy is threatened not only by data collection but by inference: a risk model can reveal intimate patterns of family life that no one deliberately disclosed. Self-determination is eroded when algorithmic outputs narrow the options a client is offered or the discretion a worker exercises. Social justice, the paper stresses, must be substantive rather than merely formal—a system can satisfy formal nondiscrimination while still distributing burdens unevenly across already-marginalized groups, because the underlying data reflect historical inequities. Human dignity is at stake whenever a person is reduced to a risk score or a fraud probability, processed by machinery they cannot see or question. The framework asks of every deployment: does this system advance these four values, or does it quietly trade them away for administrative convenience?

From the case analyses, the paper distills a set of conditions under which AI in social work is ethically defensible. First, AI must augment practitioner judgment without displacing relational authority—the human relationship remains the locus of decision, and the algorithm serves as one input among many. Second, AI must advance substantive rather than merely formal equity, which requires auditing outcomes across groups and correcting for the inequities embedded in historical data. Third, AI must be embedded in contestable, auditable institutions: affected people must be able to challenge algorithmic decisions, independent bodies must be able to inspect the systems, and the terms of deployment must remain politically revisable rather than locked in by technical opacity. Where these conditions fail, the same technology crosses from augmentation into automation, and the paper’s analysis suggests that resistance is then not Luddism but an ethical obligation grounded in the profession’s core commitments.

The Robodebt and SyRI episodes illustrate what failure looks like. Robodebt automated income-matching and debt issuance at scale, shifting the burden of proof onto vulnerable recipients and generating unlawful debts before a royal commission exposed the scheme’s flaws. SyRI’s opaque data-mining targeted low-income neighborhoods and was ruled in violation of the European Convention on Human Rights by a Dutch court in 2020. Both systems were backstage, uncontestable in practice, and structurally biased toward suspicion of the poor—precisely the configuration the framework flags as automated welfare. By contrast, the paper’s treatment of digital-care technologies suggests a more promising frontier, provided that monitoring remains genuinely consensual, that data flows serve the person being cared for rather than only the institution, and that human contact is not replaced by sensor readouts. The Korean crisis-household detection case sits between these poles, showing how administrative data can surface families in need—but only if detection triggers supportive outreach rather than punitive intervention.

The paper’s contribution is threefold, and its implications reach beyond social work. It reframes the field’s debate from replacement versus resistance to augmentation versus automation, a shift that turns a stale binary into a designable and governable distinction. It supplies a reusable diagnostic matrix that practitioners, agencies, and regulators can apply to concrete systems before deployment rather than after harm. And it integrates Korean, European, and North American evidence within a single analytic lens, demonstrating that the dilemmas of the digital welfare state are global even as their legal and institutional responses remain local. As governments worldwide race to embed AI in public services, the framework’s central warning is worth stating plainly: the question is never whether AI can make welfare administration faster or cheaper, but whether it leaves the human relationship—the actual substance of social work—intact. The research was supported by the Asan Foundation and a Gachon University research fund, and was presented at the Asan Foundation Symposium in Seoul in May 2026.

Subject of Research: An ethical framework for evaluating artificial intelligence as augmentation or automation in social work

Article Title: An ethical framework for assessing artificial intelligence as augmentation or automation in social work

Article References: Hwang, K. (2026). An ethical framework for assessing artificial intelligence as augmentation or automation in social work. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00463-x

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00463-x

Keywords: artificial intelligence, social work ethics, augmented intelligence, predictive risk modelling, algorithmic decision-making, digital welfare state, relational practice, care ethics, anti-oppressive practice, Allegheny Family Screening Tool, Robodebt, SyRI