波兰尼“脱嵌”论断下制度脱离实践的失效：数字治理AI规制与高校场景规制文本、技术逻辑及师生交互与算法流程的“制度脱嵌”

Despite the expanding discourse on corporate digital responsibility (CDR), service firms are not perceived as using AI responsibly. Consequently, customers prefer governments more than firms to regulate AI, whereas AI service firms must strive to achieve legitimacy among customers. To address the dearth of relevant research, we conducted a multimethod investigation comprising four studies. The analysis of multinational survey data and a Facebook Ads experiment showed that customers’ concern about AI (“AI concern”) increases their preference for government regulation versus firms’ self-regulation of AI. In the nine countries sampled, the preference for government regulation was stronger in materialist (vs. post-materialist) cultures and where regulatory quality and technology use were high, but weaker where rule-of-law was high. Subsequently, we examined whether signaling CDR (as self-regulation) and compliance with government regulation mitigate the negative downstream impact of AI concern. When interacting with a custom-built service chatbot, participants’ AI concern reduced their willingness to share data. However, this effect was attenuated by signaling compliance with government regulation (EU AI Act), which also decreased customers’ perceived vulnerability. Hence, rather than positioning CDR efforts solely as self-regulation, AI service firms can build legitimacy by proactively, voluntarily, and explicitly incorporating government regulations into their CDR implementation. Graphical abstract

ABSTRACT We are already living in an algorithmic society. AI policies and regulations are now emerging at the same time as more is learned about the implications of bias in machine learning sets, the surveillance risks of smart cities and facial recognition, and automated decision-making by government, among many other applications of AI and machine learning. Each of these issues raises concerns around ethics, privacy, and data protection. This paper introduces some of the key AI regulatory developments to date and engagement by libraries in these processes. While many AI applications are largely emergent and hypothetical in libraries, some mature examples can be identified in research literature searching, language tools for textual analysis, and access to collection data. The paper presents a summary of how library activities such as these are represented in national AI plans and ways that libraries have engaged with other aspects of AI regulation including the development of ethical frameworks. Based on the sector's expertise in related regulatory issues including copyright and data protection, the paper suggests further opportunities to contribute to the future of ethical, trustworthy, and transparent AI.

In popular discussions, the nuances of AI are often abridged as “the algorithm”, as the specific arrangements of machine learning (ML), deep learning (DL) and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. What is clear, however, is that AI embedded on social media functions to recommend content, personalize ads, aggregate news stories, and moderate problematic material. It is also increasingly apparent that individuals are concerned with the uses, implications, and fairness of algorithmic systems. Perhaps in response to concerns about “the algorithm” by individuals and governments, social media platforms utilize transparency initiatives and official statements, in part, to deflect official regulation. In the following paper, I draw from transparency initiatives and statements from representatives of Facebook and TikTok as case studies of how AI is embedded in these platforms, with attention to the promotion of AI content moderation as a solution to the circulation of problematic material and misinformation. This examination considers the complexity of embedded AI as a material-discursive apparatus, predicated on discursive techniques—what is seeable, sayable, knowable in a given time period—as well as the material arrangements—algorithms, datasets, users, platforms, infrastructures, moderators, etc. As such, the use of AI as part of the immensely popular platforms Facebook and TikTok demonstrates that AI does not exist in isolation, instead functioning as human–machine ensemble reliant on strategies of acceptance via discursive techniques and the changing material arrangements of everyday embeddedness.

This paper aims to advance a comprehensive sociotechnical framework for addressing fairness and bias in artificial intelligence (AI) systems, recognizing that purely technical solutions are insufficient to ensure equitable AI deployment across sectors such as hiring, lending and criminal justice. This study critically evaluates existing technical solutions for mitigating bias, highlighting their limitations in addressing real-world sociocultural contexts. A sociotechnical framework that combines algorithmic techniques, human oversight, regulatory frameworks and stakeholder engagement is proposed. This study presents a multi-component framework that integrates technical debiasing methods, stakeholder engagement, human oversight, regulatory compliance and continuous evaluation. The framework demonstrates that combining technical expertise, social science insights and diverse stakeholder perspectives leads to more effective bias mitigation and fairer AI systems. Although the framework provides a theoretical foundation, its practical implementation across different contexts and organizations requires further empirical validation. Future research should focus on measuring the effectiveness of this framework in real-world applications. This paper advances the field by proposing a comprehensive sociotechnical framework that bridges the gap between technical and social approaches to AI fairness, providing practical guidelines for organizations while acknowledging the complexity of implementing fair AI systems.

Abstract Border artificial intelligence (AI)—biometrics-based AI systems used in border control contexts—proliferates as common tools in border securitization projects. Such systems classify some migrants as posing risks like identity fraud, other forms of criminality, or terrorism. From a human rights perspective, using such risk framings for algorithmically facilitated evaluations of migrants’ biometrics systematically calls into question whether these kinds of systems can be built to be trustworthy for migrants. This article provides a thought experiment; we use a bottom-up responsible design lens—the guidance-ethics approach—to evaluate if responsible, trustworthy Border AI might constitute an oxymoron. The proposed European AI Act only limits the use of Border AI systems by classifying such systems as high risk. In parallel with these AI regulatory developments, large-scale civic movements have emerged throughout Europe to ban the use of facial recognition technologies in public spaces to defend EU citizens’ privacy. The fact that such systems remain acceptable for states’ usage to evaluate migrants, we argue, insufficiently protects migrants’ lives. In part, we argue that this is due to regulations and ethical frameworks being top-down and technology driven by focusing more on the safety of AI systems than on the safety of migrants. We conclude that bordering technologies developed from a responsible design angle would entail the development of entirely different technologies. These would refrain from harmful sorting based on biometric identifications but would start from the premise that migration is not a societal problem.

Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking.

… disembedded from local institutions. Unable to contact each other, these workers cannot bargain collectively. Moreover, even if such dispersed workers obtained the means to contact …

This article examines the impact of algorithmic management on individual freedom. To orient this exploration, I draw on the (feminist) conception of liberty as the choosing subject. The central suggestion is that algorithmic management poses a serious threat to an indispensable part of the freely choosing subject: namely, it degrades the ability of subordinates to reasonably foresee the consequences of their choices and consequently, fully realise their personality. I call this phenomenon the ‘foresight endangerment problem’ and argue that it has both a technical and a social face. The technical face highlights the inherent unpredictability of advanced algorithms, including those that execute managerial functions. This issue is further complicated by the fact that as algorithms become more resilient and useful, their outputs grow increasingly opaque and unpredictable—what some refer to as the resilience-predictability paradox. The technical face is made manifest in the reported experiences of workers in the gig economy who describe experiencing unpredictable managerial decisions that they cannot anticipate nor easily contest. Subjection to such managerial randomness erodes their ability to make informed choices in service of their personal goals. The social face emphasises the consequences of disembedding managerial power from social relationships between humans to asocial relationships between humans and software. Subordinates of human managers enjoy a vast number of tools to predict managerial thinking that arise from the intricate and complex processes of social interaction. The disembedding process forecloses the use of these tools and fundamentally undermines the capacity of subordinates to promote their ends through free choice.

Amidst the rapid rise of gig economy platforms, gig workers increasingly report feelings of mistrust, anxiety, and profound fear under opaque and abusive algorithmic management. This article introduces the concept of ‘algorithmic paranoia’ to capture the negative affective experiences stemming from workers' perceptions of algorithmic management as non‐transparent, arbitrary, and retaliatory. Drawing on the concept of organisational paranoia from sociology and organisation studies, we theorise how workers' adverse experiences breed mistrust and suspicion toward both human and nonhuman actors on the platform. This culminates in intense feelings of persecution and anticipations of harm, which workers strive to cope with through hypervigilance and self‐protective actions aimed at pre‐empting anticipated threats. Our study contributes to existing literature by emphasising the role of affect in workers' responses to algorithmic management, highlighting the self‐reinforcing dynamics among perceptions of abusive management, negative affective experiences, and preventive, self‐preserving actions. We base our findings on an abductive analysis of data from 53 in‐depth interviews with creative freelancers on gig economy platforms and conversations from an online community forum.

… This paper introduces the concept of algorithmic obedience to … We formalize this through the Theorem of Disembedded Syntactic … His theory of institutional facts further claims that social …

… In our cases, disembedding most often took place with respect to the institutional dimension, … by blending local knowledge with algorithmic evaluation, digital addressing systems compel …

Driven by the escalating global emphasis on research impact, contemporary science policy has solidified around a valorisation imperative that increasingly treats knowledge as a strategic asset rather than a common heritage. This paper interrogates the ethical paradox that emerges as research outcomes are progressively viewed through the prism of economic utility, clashing with the foundational mission of science as a public good. By conceptualising publicly funded knowledge as a Polanyian fictitious commodity, the analysis deconstructs the institutional mechanisms that facilitate its disembedding from the social commons. Through a critical examination of two emblematic cases—the commercialisation of CRISPR gene‐editing and the technical enclosure of foundational AI—the inquiry reveals how pathways of legal and technical gatekeeping transform social resources into proprietary assets. Critically, it demonstrates that these valorisation regimes co‐produce systemic injustices, ranging from prohibitive price tags for essential therapies to the concentration of unaccountable digital power. In response to these structural failures, the paper proposes a normative framework wedding the principle of value pluralism with the procedural engine of Responsible Research and Innovation. By advancing these principles, it contributes to a critical reimagining of research valorisation, charting a principled course to better align scientific practice with its noble calling to serve the common weal.

This chapter explores the technopolitical dynamics underpinning the monopolistic rise of data-opolies—dominant tech corporations such as GAFAM—and their implications for democracy in the age of AI. Drawing on Calzada’s conceptual framework and action research, it investigates how algorithmic governance in smart cities displaces democratic deliberation, eroding civic trust and transparency. The chapter critically engages with Web3 technologies—blockchain, DAOs, and data cooperatives—as possible countermeasures to platform capitalism, while remaining attentive to their ideological tensions, especially the crypto-libertarian vision of “Network States” à la Srinivasan. By linking the governance of AI systems with Richard R. Nelson’s “Moon and Ghetto” metaphor, the chapter underscores a key paradox: technological innovation soars, yet foundational democratic deficits remain unresolved. Through a multidisciplinary lens, it interrogates how decentralized infrastructures might reclaim digital sovereignty and foster more equitable innovation systems. The chapter concludes by proposing a pluralistic governance model rooted in algorithmic transparency, data ethics by design, and multistakeholder engagement—critical steps toward re-democratizing the datafied city.

… There has been a dis-embedding of individuals from … production, financialization, and algorithmic governance, we argue that … Investors and financial institutions had more resources at …

intellectual structure of smart city research. These studies and their findings are examined

… as technologically enhanced disembedding and integration … formal models, metrics, and algorithms. This brings us to the … on the institutional side of the emerging algorithmic urbanism. …

… ’s technologies. The consequence of this growing gap between the pace of technology and … , institutions and processes to regulate emerging technologies. The two basic options for …

… This holistic view emphasizes that managing the gap between technology and standards isn’… to bridge the gap between technology, standards, and regulatory frameworks by adopting a …

Designed to close the ubiquitous gap between law on the books and law in action, management systems locate the standard setting and implementation of regulation within the regulated organization itself. Despite efforts to more closely couple aspirations and performance, the gap re-emerges because the exigencies of practical action exceed the capacity of system prescriptions to anticipate and contain them. Drawing on data from a six-year ethnographic study of the creation and implementation of an environment, health, and safety management system, this article iden- tifies relational regulation as the approach used by front-line managers to govern the gap: keeping organizational activities within an acceptable range of variation close to regulatory specifications. We identify four practices - narrating the gap, inquiring without constraint, integrating pluralistic accounts, and crafting pragmatic accommodations - and three conditions under which actors may develop a sociological orientation to enact relational regulation. Overall, the article concludes that themechanismforassuringcomplianceresidesintheapprehensionof relationalinterdependencies rather than the management system per se. rego_1100 14..42

… platforms for SMEs, and blockchain technologies. We develop a meta-… as regulatory innovation intermediaries. We describe the evolutions and interrelations of new technologies and …

Administrations mandated to adopt audit technologies frequently achieve formal compliance while sustaining persistent gaps between policy and operational practice, a pattern that individual-level technology acceptance models cannot explain. This theory-building study develops an integrated framework combining institutional logics (IL) with Williamson’s new institutional economics (NIE) to explain how sociocultural pressures and economic constraints jointly produce and sustain these gaps. Using an abductive research design, we analyze Computer-Assisted Audit Tools and Techniques (CAATTs) implementation in Indonesia’s tax administration through document analysis and focus group discussions spanning three decades, constructing five propositions that specify the conditions under which collaborative, competing, and decoupling logics emerge, persist, and transition. The analysis reveals that regulatory absence produces collaborative logics as practitioners pool search costs through informal coordination, regulatory formalization triggers competing logics by shifting costs from search to enforcement, and the resulting cost gap between symbolic and substantive compliance produces decoupling that persists until governance investments reduce it. The study contributes to compliance risk governance by identifying the causal mechanisms through which institutional pressures and economic constraints interact during mandated technology adoption, offering testable propositions applicable to regulated organizations managing policy-practice gaps.

This study addresses the urgent challenge of safeguarding sensitive health data in today’s digital age by proposing a novel, integrated data protection framework that synthesises six critical pillars—technology, policy, cybersecurity, legal frameworks, governance, and risk assessment—into a unified socio-technical model. Unlike existing piecemeal approaches, this framework is designed to bridge the gap between regulatory requirements and practical implementation through measurable, engineering-based solutions. Healthcare organisations face persistent difficulties in aligning innovation with secure and compliant practices due to fragmented governance and reactive cybersecurity measures. This paper aims to empirically validate the effectiveness of the proposed framework by quantitatively analysing causal relationships between its components (such as between governance and compliance) using advanced statistical methods, including exploratory factor analysis (EFA) and Partial Least Squares Structural Equation Modelling (PLS-SEM). A survey of healthcare professionals across multiple countries revealed significant gaps between regulatory expectations and operational realities, underscoring the need for harmonised strategies. The results demonstrate strong causal linkages between governance, cybersecurity practices, and compliance, validating the framework’s robustness. This research contributes to the fields of digital health, information systems, industrial engineering, and electronic governance by offering a scalable, empirically tested model for socio-technical data protection. The findings provide actionable strategies for policymakers, system architects, and digital infrastructure designers.

The rapid expansion of predictive algorithms across social, economic, and governmental systems has intensified concerns about the reproduction of inequality and the emergence of new forms of algorithmic governance. This paper examines how predictive technologies—ranging from risk-assessment tools and automated hiring systems to credit-scoring platforms and predictive policing—systematically shape opportunities, access, and social outcomes. Drawing on insights from sociology, critical data studies, and surveillance theory, the study analyses the mechanisms through which algorithmic systems encode historical biases, amplify structural disadvantages, and normalise surveillance as a mode of social control. The paper argues that predictive algorithms operate within unequal data infrastructures that disproportionately disadvantage marginalised groups, reinforcing patterns of racialised, gendered, and class-based exclusion. Moreover, the opacity of algorithmic decision-making and the rise of automated governance shift power away from public accountability and towards computational forms of authority controlled by states and corporations. By situating predictive technologies within broader socio-political contexts, this paper highlights the urgent need for transparent modelling practices, anti-bias regulation, and equitable data governance frameworks. The findings contribute to ongoing debates about digital injustice and provide a sociological foundation for understanding how algorithmic systems reshape power, surveillance, and inequality in the digital age.

… Once we think of algorithmic governance in terms not merely of algorithms but of the whole system, it is easier to see how the automatically adjusted credit score (Zarsky, 2016), …

This article considers the managerial aspect of digital transformation - various programs and infrastructure that have recently received the general name “algorithmic management”. The boom in the use of such tools occurred during the covid-19 pandemic as a unique set of circumstances for the digitalization of human life. The authorities of several countries monitored their citizens’ behavior, including with the QR-code systems that limited their rights, in the fight against the spread of the covid-19, which has caused discussions and even protests. Businesses accelerated their digital transformation in HR management due to government restrictions and lockdown measures and to production needs in the new conditions. Quarantines are over, but the active development of algorithmic management continues; it extends beyond the platform economy and plays an integral role in Industry 4.0, which makes the study of algorithmic management relevant and timely. A significant contribution to understanding algorithmic management was made by the report of the experts from the European Commission and International Labor Organization. Based on the relevant publications up to 2022, they suggested giving up the narrow understanding of algorithmic management as a platform economy issue; however, most studies are still based on this interpretation. The article presents a broader definition to identify additional social contradictions and challenges of digital transformation. The author considers algorithmic management in the perspective of sociology of management and sociology of technology, in particular the works of A. Feenberg and P. Edwards. The approach of sociology of technologies studies (STS) allowed the author not only to analyze the events of the recent pandemic but also to consider the future of such technologies under the transition towards Industry 4.0. The article identifies three elements of algorithmic management together with hidden social-managerial biases and contradictions related to their implementation and shows how the new approach integrates direct and indirect control in management.

Algorithmic governance affects individuals’ reality construction and consequently social order in societies. Vague concepts of algorithmic governance and the lack of comprehensive empirical insights into this kind of institutional steering by software from a user perspective may, however, lead to unrealistic risk assessments and premature policy conclusions. Therefore, this paper offers a theoretical model to measure the significance of algorithmic governance and an empirical mixed-methods approach to test it in different life domains. Applying this guideline should lead to a more nuanced understanding of the actual significance of algorithmic governance, thus contributing to an empirically better-informed risk assessment and governance of algorithms.

We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider (a) barriers to legitimate and effective algorithmic governance and (b) the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplinary group. This method enabled participants to produce a framework and research agenda for those who are concerned about algorithmic governance. We outline this research agenda below, providing a detailed map of key research themes, questions and methods that our workshop felt ought to be pursued. This builds upon existing work on research agendas for critical algorithm studies in a unique way through the method of collective intelligence.

This article examines the emergence of ‘digital governance’ in public education in England. Drawing on and combining concepts from software studies, policy and political studies, it identifies some specific approaches to digital governance facilitated by network-based communications and database-driven information processing software that are being discursively promoted in education by cross-sectoral intermediary organizations. Such intermediaries, including National Endowment for Science, Technology and the Arts, Demos, the Innovation Unit, the Education Foundation and the Nominet Trust, are increasingly seeking to participate in new digitally mediated forms of educational governance. Through their promotion of network-based pedagogies and database-driven analytics software, these organizations are seeking to delegate educational decision-making to socio-algorithmic forms of power that have the capacity to predict, govern and activate learners' capacities and subjectivities.

Purpose This study examines how algorithmic governance reshapes workflows, decision rights, accountability boundaries, and professional roles within public organizations. It …

Governance scholarship overwhelmingly treats artificial intelligence as a tool — an object to be regulated, audited, or aligned with human values. This article argues that when organizations treat the outputs of AI agents as institutionally binding, these systems cross a threshold from instruments into institutional actors. Drawing on Goffmans interaction order and Meyer and Rowans institutional isomorphism, we develop a diagnostic framework that specifies when and how AI agents acquire practical actorhood within organizations. We formalize a 2x2 Actorhood Matrix along two axes — discretion granted and institutional embedding — yielding four system types: Tool, Infrastructure, Shadow Actor, and Institutional Actor. The Actorhood Matrix is proposed as a reusable diagnostic method for identifying when AI systems cross the institutional threshold from tools to role occupants. Applying this framework to five empirical cases demonstrates that institutional actorhood is not a property of technical sophistication but of organizational role assignment. We identify a critical transition zone where override rates fall below ten percent and propose four testable propositions linking discretion, embedding, and temporal persistence to governance outcomes.

Abstract With the advent of artificial intelligence, stakeholders and experts cede their policy decisions for human affairs to computer algorithms in algorithmic governance. However, they face a new material principal-agent problem, which occurs between computer scientists as principals and computer algorithms as agents. Drawing upon new materialism, this study investigates informational asymmetry, malfeasance, agency relationships, and solutions related to the principal-agent problem. The inscrutability of computer algorithms is central to the notion of informational asymmetry and their relational agency is related to the notion of malfeasance. The principal-agent relationship is viewed as the output of socio-material assemblages in which computer scientists strive to build trust with computer algorithms. The inscrutability of computer algorithms coupled with their performativity would make it challenging for human principals to ascertain the malfeasance of computer algorithms as agents, thereby forming the material principal-agent problem. Finally, this study recommends an incremental, precautionary, and technologically pluralist approach to cope with this problem.

We propose here a conceptual framework by which to analyze legal-regulatory problematics of algorithmic decision-making systems, focusing on mechanisms of value production in their design and deployment. An aim of our intervention is to develop an investigative model for application to algorithmic decision systems with regulatory effects, including predictive artificial intelligence applications and recommender systems that filter data and suggest courses of action. Technical systems that integrate complex algorithmic techniques perform critical and sensitive functions that are both object and instrument of regulatory governance, functions such as predicting behavior, steering information flows, assessing risk, etc. These functions, however, are not simple or static phenomena, but rather contextual, partial performances of complex socio-technical dynamics. One of our interests is to discern what is valorized in this new regulatory ecology. Accordingly, we are sketching a framework to target terms and tokens of value as they are produced, reproduced, incorporated, and translated among design processes, legal practices and background conditions structuring their use. Rather than asking which values AI should satisfy in contested governance contexts, we address conceptually prior questions concerning how values manifest and ‘map’ among context-sensitive computational and social processes in the first place. Furthermore, current research often takes for granted that an AI application is produced against the backdrop of a stable and pre-defined set of values and legal practices. Existing research does not yet adequately account for the ways in which laws and values as produced in and through the ecology of the AI application differ from idealized presuppositions assumed to preexist development of the latter. For the purpose, our contribution engages three broad lines of inquiry: one, we take forward calls for a materialized study of law, such as put forward broadly by Alain Pottage, and as put forward more recently and specifically with respect to computational technologies by Mireille Hildebrandt, among others; two, we contribute to the elaboration of a critical practice for AI, in the tradition of Philip Agre; and three, our attention to assemblages potentially contributes to debates over techno-regulation or regulation by design.

Internet-based services that build on automated algorithmic selection processes, for example search engines, computational advertising, and recommender systems, are booming and platform companies that provide such services are among the most valuable corporations worldwide. Algorithms on and beyond the Internet are increasingly influencing, aiding, or replacing human decision-making in many life domains. Their far-reaching, multifaceted economic and social impact, which results from the governance by algorithms, is widely acknowledged. However, suitable policy reactions, that is, the governance of algorithms, are the subject of controversy in academia, politics, industry, and civil society. This governance by and of algorithms is to be understood in the wider context of current technical and societal change, and in connection with other emerging trends. In particular, expanding algorithmizing of life domains is closely interrelated with and dependent on growing datafication and big data on the one hand, and rising automation and artificial intelligence in modern, digitized societies on the other. Consequently, the assessments and debates of these central developmental trends in digitized societies overlap extensively. Research on the governance by and of algorithms is highly interdisciplinary. Communication studies contributes to the formation of so-called “critical algorithms studies” with its wide set of sub-fields and approaches and by applying qualitative and quantitative methods. Its contributions focus both on the impact of algorithmic systems on traditional media, journalism, and the public sphere, and also cover effect analyses and risk assessments of algorithmic-selection applications in many domains of everyday life. The latter includes the whole range of public and private governance options to counter or reduce these risks or to safeguard ethical standards and human rights, including communication rights in a digital age.

… algorithms enact modes of governing dictated by those who govern; social democrats use algorithms to enhance social … their governing of programmers and coders; and (b) algorithms …

… assisting in student distribution in a classroom; algorithms predicting where and when … on Netflix; algorithms detecting possible fraud in social benefits; algorithms identifying privacy …

Artificial Intelligence (AI) has been highlighted as a potentially disruptive force across several industries and in higher education. Research has suggested that education and upskilling of citizens should adopt a broad approach, not only focusing on experts, for better nationwide AI readiness. This study investigates the content of AI policies in higher education by analysing official documents from Swedish universities. It identifies key themes and patterns, comparing them with related research and international guidelines. Expanding on the results of the study, it develops a dynamic alignment model for higher education AI policy (DAMHEAP) which, grounded in institutional theory, highlights strategic, pedagogical, ethical and legal, operational and adaptive alignments. This model, together with 10 practical recommendations, provides a roadmap for higher education institutions to develop and maintain AI policies that are pedagogically relevant, ethically responsible and adaptive to technological change.

… higher education institutions (HEIs) are addressing GenAI through guidelines, policy … Thus, the aim of this study was to explore the guidelines, policy documents, and information that …

In recent years, the European Union has advanced towards responsible and sustainable Artificial Intelligence (AI) research, development and innovation. While the Ethics Guidelines for Trustworthy AI released in 2019 and the AI Act in 2021 set the starting point for a European Ethical AI, there are still several challenges to translate such advances into the public debate, education and practical learning. This paper contributes towards closing this gap by reviewing the approaches that can be found in the existing literature and by interviewing 11 experts across five countries to help define educational strategies, competencies and resources needed for the successful implementation of Trustworthy AI in Higher Education (HE) and to reach students from all disciplines. The findings are presented in the form of recommendations both for educators and policy incentives, translating the guidelines into HE teaching and practice, so that the next generation of young people can contribute to an ethical, safe and cutting-edge AI made in Europe.

… As a well-structured governance framework, it helps institutions develop AI policies that foster ethical, responsible AI use and competence-building in faculty and students. …

The integration of generative artificial intelligence (GAI) in higher education presents significant opportunities and challenges, yet a systematic understanding of its multifaceted impact remains fragmented. This systematic review synthesizes empirical evidence on GAI implementation to propose an integrated framework of best practices. We address four research questions examining student and faculty perceptions, institutional integration strategies and barriers, ethical risks and pedagogical innovation potential. Following PRISMA methodology and guided by the TPACK framework, we analysed 125 empirical studies published between 2021 and 2025. Our findings reveal four distinctive patterns. First, the ‘curriculum integration paradox’ shows that institutional investments in faculty development yield a negligible correlation ( r  = 0.12) with pedagogical transformation. Second, geographical analysis uncovers divergence between the Global North's focus on creativity enhancement and the Global South's challenges with fundamental access. Third, ethical risks form an interconnected ecosystem where academic integrity, privacy, bias and equity interact complexly. Fourth, persistent tension between enthusiasm (73% student adoption) and faculty resistance reflects deeper epistemological uncertainties. Discussion highlights that GAI's transformative capacity extends beyond automation to redefining pedagogical roles and fostering metacognition, yet realizing this potential requires comprehensive ecosystem development. We conclude that successful GAI integration demands more than technological adoption—it requires fundamental transformation of educational paradigms through contextually sensitive, ethically grounded strategies that balance innovation with equity. The proposed framework emphasizes systemic integration and participatory governance to create inclusive, effective educational futures. Rationale for this study: The rapid integration of generative artificial intelligence (GAI) in higher education lacks an updated synthesis addressing systemic patterns, geographical disparities and the gap between institutional investment and actual pedagogical transformation. Why the new findings matter: Our findings reveal a ‘curriculum integration paradox’ and an interconnected ethical ecosystem, providing an empirical framework to guide evidence‐based policies and practices in GAI implementation. Implications for practitioners, policy‐makers, and researchers: For practitioners, findings emphasize evolving from knowledge transmitters to learning experience designers, leveraging GAI to foster critical thinking and metacognition. For policymakers, results underscore the urgency of developing ethical, inclusive and context‐sensitive governance frameworks that address global digital divides while ensuring educational equity. For researchers, we identify critical gaps, including the absence of longitudinal studies and the need for integrated theoretical models connecting technology, pedagogy and ethics within expanded TPACK frameworks. The curriculum integration paradox particularly demands intervention studies exploring how professional development can translate into meaningful pedagogical transformation across diverse educational contexts.

This study investigates the dynamics of policy implementation and regulatory challenges in managing civil service resources within the evolving framework of digital governance. The research aims to interpret how digital transformation reshapes administrative structures, human resource management, and regulatory coherence in the public sector. Employing a qualitative descriptive approach based on a systematic literature review, the study synthesizes empirical and conceptual evidence from recent academic and institutional publications between 2013 and 2025. Data were analyzed thematically through interpretive synthesis to identify recurring patterns of policy adaptation, institutional capacity, and regulatory reform across different governance contexts. The findings reveal that digital governance transforms policy implementation from a linear bureaucratic process into an adaptive, data-driven, and network-based system that demands interagency coordination and technological proficiency. Regulatory challenges emerge from outdated legal frameworks, fragmented institutional mandates, and limited digital literacy among civil servants, leading to uneven implementation outcomes. The study highlights that effective civil service resource management requires integrating digital competency frameworks, ethical data governance, and agile leadership within coherent regulatory systems. Theoretically, this research contributes to modernizing implementation theory by embedding digital institutionalism and adaptive capacity concepts, while managerially, it offers a model for aligning human resource policies with sustainable digital transformation. The results affirm that sustainable digital governance depends on the continuous interaction between policy innovation, regulatory adaptability, and institutional learning.

The European Union Artificial Intelligence Act, Regulation (EU) 2024/1689 of the European Parliament and of the Council, dated 13 March 2024, on artificial intelligence, marks the first comprehensive legal framework for artificial intelligence. It establishes a risk-based regulatory architecture that distributes obligations across diverse actors in the AI value chain. While its provisions emphasize proportionality and trustworthiness, significant asymmetries emerge between technologically advanced providers and low-capacity actors such as SMEs, municipalities, and public authorities. This article conducts a structured literature review of regulatory, ethical, and governance sources to examine how compliance responsibilities are operationalized across risk tiers and actor roles. In particular, it analyses the Assessment List for Trustworthy AI (ALTAI) as a soft-law ethics instrument, the EU AI Act as hard law, and comparative frameworks such as ISO/IEC 42001, the NIST AI Risk Management Framework, and the OECD AI Principles. The findings reveal gaps in enforceability, proportionality, and auditability that limit the accessibility of compliance for under-resourced organizations. To address these gaps, the article outlines the need for lightweight compliance frameworks that extend ALTAI’s normative scaffolding into actionable and auditable processes. By mapping role-specific obligations against the structural capacities of actors, the analysis contributes to ongoing debates on operationalizing trustworthy and lawful AI in the European context.

… the potential mismatch between pre-digital era laws governing public … governance actor wielding policy, oversight, and regulatory controls. It is far from certain that in the case of digital …

Abstract This Article examines the regulatory state through the lens of evolving political economy, arguing that a significant reconstruction is now underway. The ongoing shift from an industrial mode of development to an informational one has created existential challenges for regulatory models and constructs developed in the context of the industrial economy. Contemporary contests over the substance of regulatory mandates and the shape of regulatory institutions are most usefully understood as moves within a larger struggle to chart a new direction for the regulatory state in the era of informational capitalism. A regulatory state optimized for the information economy must develop rubrics for responding to three problems that have confounded existing regulatory regimes: (1) platform power — the power to link facially separate markets and/or to constrain participation in markets by using technical protocols; (2) infoglut — unmanageably voluminous, mediated information flows that create information overload; and (3) systemic threat — nascent, probabilistically-defined harm to be realized at some point in the future. Additionally, it must develop institutions capable of exercising effective oversight of informationera activities. The information-era regulatory models that have begun to emerge are procedurally informal, mediated by networks of professional and technical expertise that define relevant standards, and financialized. Such models, however, also have tended to be both opaque to external observation and highly prone to capture. New institutional forms that might ensure their legal and political accountability have been slow to develop.

… legitimacy stemming from regulatory mismatches. The … neatly with existing regulations governing taxicab and PHV … regulatory permission despite mismatches between its digital…

… -making is a natural trend in contemporary organizational practices. There is evidence that job applicants react negatively toward AI-based selection practices [25]. In our mental …

… Transparency, as an ideal, can be traced through many histories of practice. From … We can think of the emblematic case where an organization is asked to share its records, so it prints …

Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize algorithms as value-laden, rather than neutral, in that algorithms create moral consequences, reinforce or undercut ethical principles, and enable or diminish stakeholder rights and dignity. In addition, algorithms are an important actor in ethical decisions and influence the delegation of roles and responsibilities within these decisions. As such, firms should be responsible not only for the value-laden-ness of an algorithm but also for designing who-does-what within the algorithmic decision. As such, firms developing algorithms are accountable for designing how large a role individual will be permitted to take in the subsequent algorithmic decision. Counter to current arguments, I find that if an algorithm is designed to preclude individuals from taking responsibility within a decision, then the designer of the algorithm should be held accountable for the ethical implications of the algorithm in use.

… in algorithmic accountability. Wieringa’s systematic review shows that calls for algorithmic accountability … is formally declared to whether the organization has the structures and practices …

… of recognizing that accountability practices and responsibility practices are … practices of holding people accountable within social organizations and that these accountability practices …

… system produces outputs that are designed to be used as part of organizational practices’ (p. 55). This ‘moment-to-moment’ sensemaking situates the agency of different actors in an …

The rise of algorithmic management (AM) is helping to transform work and employment relationships, creating new challenges and opportunities alike. AM leverages machine‐learning algorithms to help automate managerial functions. This raises key questions about its impact on work, organisations, and the broader society. This paper synthesises existing research on AM and categorizes scholarly insights into five theoretical perspectives: AM as a surveillance and control system, as a neutral tool, as an agentic boss, as a socio‐technical process, and AM as a contradictory unity. While AM enhances coordination and efficiency, it also raises concerns such as pervasive surveillance, bias, dehumanization and worker alienation. We highlight the tensions between control and autonomy, transparency and opacity, and efficiency and fairness, illustrating the paradoxical nature of AM. This paper proposes a future research agenda, calling for ethical governance and responsible design of algorithmic systems to reap the benefits of AM while managing potential risks and mitigating harms.

… that we need to study organizational practices to understand the effects of the use of algorithms. This is why we use the term ‘algorithmization’ as an organizational process rather than …

Abstract The wicked challenge of designing accountability measures aimed at improving algorithmic accountability demands human-centered approaches. Based on one of the most common definitions of accountability as the relationship between an actor and a forum, this article presents an analytic lens in the form of actor and forum agency, through which the accountability process can be analysed. Two case studies - the Austrian Public Employment Service’s AMAS system and the EnerCoach energy accounting system, serve as examples to an analysis of accountability based on the agency of the stakeholders. Developed through the comparison of the two systems, the Algorithmic Accountability Agency Framework (A3 framework) aimed at supporting the analysis and the improvement of agency throughout the four steps of the accountability process is presented and discussed.

This article examines how algorithmic accountability is translated into action at the municipal level in the United States. Based on a review of task forces, ordinances, and policy toolkits from New York City and Seattle, I demonstrate the ways municipalities and local publics operationalize abstract notions of accountability. Municipal interventions often prioritize revealing computational tools (transparency) and their effects on people (impact assessments). While these two forms of accountability are crucial, they may neglect to examine institutions—and how they change—as they incorporate automated decision systems. I thus propose a political-economic approach that recognizes algorithmic systems as part of municipal institutions and focuses on their role in intensifying data collection and commodification between public agencies and markets. I argue that algorithmic accountability, especially in public agencies, needs to focus on incompetence and asymmetries of power within a network of governments, tech companies, community groups, and technologies. With a mix of transparency, impact assessments, and political economic review, the paper proposes a more comprehensive assessment of automated decision systems through their development, procurement, use, impact, and decommissioning.

Abstract This article examines the impact of bureaucratic accountability structures on public servants’ attitudes toward algorithmic decision-making systems (ADS) in public administration, particularly within the under-researched context of the Global South. It employs accountability theory and algorithm aversion theory to explore how internal organizational accountability influences the adoption of technology in governance. The main question, “How do bureaucratic accountability structures affect public servants’ disposition toward algorithm use?” is investigated through a case study at the Chilean Instituto de Previsión Social (IPS). Findings indicate that well-defined accountability structures can significantly enhance trust in ADS, leading public officials to favor algorithmic decisions due to their perceived reliability and clarity in role responsibilities. With this, the article contributes to an under-researched dimension by linking accountability structures with technology adoption in public administrations, offering insights that could help shape organizational and policy frameworks for integrating ADS more effectively in governance settings.

For decades, the competency paradigm has served as a dominant boundary condition in leadership theory. Predicated on the assumption that organizational outcomes are the determinative outcomes of personal agency and interpersonal dynamics, models such as transformational leadership (Bass, 1985) and leader–member exchange (LMX; Graen and Uhl-Bien, 1995) have tended to bracket a leader's interpersonal traits from the performance realities of the architectures leaders authorize. As a result, these frameworks can be structurally underspecified for the present moment: the rise of the algorithmic agent as a primary organizational actor whose effects are mediated through scale, opacity and automated optimization.Grounding our inquiry into sociotechnical systems (STS) theory (Trist, 1981), this volume treats leadership in the AI era as an emergent property of interaction between social actors and technical infrastructures. We argue that a traditional unit of analysis in organization development (OD), the leader's mindset, no longer suffices on its own when algorithmic architectures function as consequential organizational actors. Following Drucker's (1954) framing of management as a social organ responsible for institutional performance, we move beyond a primarily utilitarian inquiry into how leaders use AI to improve productivity and instead examine how organizational responsibility is operationalized, or surrendered, through the architectures that leaders authorize.The urgency of this critique is no longer theoretical; it is being adjudicated in real time (Grabenstein, 2026a, b). As seen in the 2026 landmark trial in Los Angeles, Meta's CEO was confronted with alleged systemic outcomes of Instagram's architecture, sharpening what this volume terms the performance gap between stated values and observable results (Tan, 2026). Drucker's “mirror test” offers a governance-relevant ethic for this gap: ethics requires that a leader's actions remain compatible with the person they can live with seeing “in the mirror” each morning (Drucker, 1999/2005). When leaders authorize systems of infinite reach yet represent the resulting harms as operationally intractable (too complex, too diffuse or too difficult to govern), the managerial posture shifts from stewardship to authorization without effective oversight, leaving a social organ unmanaged at scale.We anchor this inquiry in the “Kaplan Paradox”: a condition in which a leader's systemic liability expands in direct proportion to algorithmic reach, even as operational transparency, and thus perceived managerial control, diminishes. The paradox is sustained by shadow text: layers of algorithmic operation that are functionally inaccessible to the human agent. When leaders authorize opaque architectures through a digital “click-to-approve” autonomy, they define an automated reality while remaining legally and socially tethered to its outcomes.As Griffin (2025) demonstrates, this “black box” can mask the deliberate ethical agency of developers – agency that management has a professional duty to govern. In agency-theoretic terms, leaders remain principals even when decision rights are delegated to technical agents; delegation changes monitoring and incentives, not responsibility for outcomes (Eisenhardt, 1989). By failing to embed the ecosystems of responsibility advocated by Stahl (2023), leaders enter a governance mode that resembles an ethical exception (Agamben, 2005), in which normal expectations for explanation, auditability and duty of care are suspended in practice. Reliance on delegation that is not readily auditable constitutes a structural breach of the duty of care, rendering leadership responsible for systemic malfunction, even when leaders experience diminished operational visibility.The papers in this part provide the diagnostic and statutory components required to repair this professional breach.If Part I provides the diagnostic audit of the “Kaplan Paradox”, Part II turns to the rigorous technical labor of the active steward, managing not only people but also the integrity of the systems through which power is exercised within the organizational control room (Drucker, 2010). Within this frame, we explicitly reject the managerial sedative – the assumption that empathy or culture alone can resolve the Kaplan Paradox. Empathy without systemic maintenance is not governance; it is performance.Although Drucker did not live to see the AI era, his core claim remains germane: managers are responsible for designing institutions to produce specific results and for maintaining the legitimacy of those results through disciplined practice (Drucker, 1954, 1973). Extending this legacy, we shift the unit of analysis from a conventional human-in-the-loop (HIL) model to a machine-in-the-human-loop (MIL) architecture, in which algorithmic agents are treated as subordinate within a shell of human stewardship (Shneiderman, 2022), maintaining that shell requires more than leadership style. It requires algorithmic discretion as a digital evolution of street-level discretion: practical judgment applied to mitigate the rigidity and externalities of rule-bound systems under real-world conditions (Lipsky, 1980).In this control room view, the leader must serve as the conscience of the architecture. Refilling the managerial vacancy created by automated delegation requires three stewardship postures derived from Drucker's functionalist ethics:Mirror test. The mirror test operates as a hard constraint against the “I didn't know” defense: if a leader authorizes an agent that acts in ways the leader would not personally endorse, the leader has failed in self-management (Drucker, 1999/2005).Feedback analysis. Feedback analysis operationalizes accountability as a method: leaders must declare the system's intent, compare expected outcomes with actual results and intervene, via manual override, when the two diverge (Drucker, 1999/2005).Pioneering the future. For Drucker, management is an organ of society charged with making the future rather than inheriting it by default (Drucker, 1954, 1973). In this sense, the “don't poke the bear” posture is not prudence but abdication: leaders cannot be passengers in machines they built but refuse to drive (Woffard, 2022).When managers do not assume this architectural responsibility, a vacancy forms: a vacuum of integrity in which both steward and user are reduced to cogs within a system whose defaults become destiny (Trist, 1981). The papers in this part examine the mechanisms by which organizations can reclaim agency through the technical labor of algorithmic discretion, re-establishing the manual override as a professional duty rather than an optional intervention.Management discipline in the AI era is defined by the manual override. We define algorithmic discretion as the digital evolution of street-level discretion required to mitigate the inherent rigidity of automated rule sets.To govern the machine loop, the steward requires constant telemetry.Together, they signal precisely when the architecture requires an active override.The greatest vulnerability of the system architect is operational opacity. As Zuboff (2019) warns, the ultimate cost of algorithmic governance is the erosion of the right to the future tense, the human capacity to act with intent.Together, this two-volume collection moves beyond the traditional boundaries of the competency paradigm to establish a formal architecture of accountability. By integrating the diagnostic rigor of an institutional audit with the functional requirements of the active steward, these papers provide a theoretical and practical roadmap for reclaiming agency within machine-mediated organizations. This transition, from managing interpersonal behavior to governing authorization logic, is not merely a technical adjustment; it is a fundamental evolution of the leadership duty of care.The Kaplan Paradox reminds us that as algorithmic reach expands, the vacuum of integrity created by automated delegation can no longer be filled by managerial sedatives or performative ethics. Instead, it requires the rigorous, technical labor of algorithmic discretion and the operational reality of the manual override. Ultimately, the control room is defined throughout this special issue as not only a technical mechanism of absolute authority but also a site of professional responsibility requiring the discipline of practice, where the machine is brought back under the stewardship of the human hand and the institutional conscience.This article is based on secondary analysis of published accounts and does not involve new research with human participants; therefore, ethics approval was not required.No new data was created or analyzed in preparation of this work. Data sharing is not applicable to this article.During the preparation of this work, the author used ChatGBT to assist with citation verification and formatting. After using this tool, the author reviewed and edited the content and takes full responsibility for the content of the published article.This Special Issue is the result of a nine-month journey that began at the International Leadership Association (ILA) Virtual AI Summit 2025, following an editorial panel discussion I facilitated. I am deeply indebted to Stefanie Johnson for her early vision in appointing me as Guest Editor and for granting me the intellectual freedom to chart the architecture for this issue. I also thank Rickard Enstroem, who provided steady stewardship and vital continuity as he assumed the leadership of the Leadership and Organization Development Journal.A project of this complexity relies on an invisible infrastructure of professional labor. I wish to extend a special thanks to my friend, colleague, and fellow editor, Suzanne Joy Clark, whose assistance in co-auditing and managing the extensive volume of reviews was indispensable to the quality of the final collection. I also wish to thank Lauren Malone and Emma Ferguson for their ongoing and remarkably patient assistance in navigating the administrative and technical hurdles of a two-volume production.The theoretical anchor of this issue, the Kaplan Paradox, was sparked by Christopher Woffard’s 2022 Wired article, which provided the necessary vocabulary for the operational invisibility I have witnessed throughout my thirty years in the technology industry. However, a concept becomes a discipline only under empirical pressure. I am indebted to the authors of these papers for their intellectual courage in reframing their research to align with this “Hard Governance” architecture.Finally, I must acknowledge the peer reviewers. Although they remain anonymous, their contributions are evident on every page. In an era when managerial sedatives often pass for scholarship, these reviewers served as the primary auditors of the logic in this issue. Their rigorous, often necessarily uncomfortable critiques ensured that our Control Room was built on a diagnostic foundation, examining the institutional duty of care in an era of automated scale. Their labor is the silent, generative heartbeat of academic discipline.

In widely used sociological descriptions of how accountability is structured through institutions, an “actor” (e.g., the developer) is accountable to a “forum” (e.g., regulatory agencies) empowered to pass judgements on and demand changes from the actor or enforce sanctions. However, questions about structuring accountability persist: why and how is a forum compelled to keep making demands of the actor when such demands are called for? To whom is a forum accountable in the performance of its responsibilities, and how can its practices and decisions be contested? In the context of algorithmic accountability, we contend that a robust accountability regime requires a triadic relationship, wherein the forum is also accountable to another entity: the public(s). Typically, as is the case with environmental impact assessments, public(s) make demands upon the forum's judgements and procedures through the courts, thereby establishing a minimum standard of due diligence. However, core challenges relating to: (1) lack of documentation, (2) difficulties in claiming standing, and (3) struggles around admissibility of expert evidence on and achieving consensus over the workings of algorithmic systems in adversarial proceedings prevent the public from approaching the courts when faced with algorithmic harms. In this paper, we demonstrate that the courts are the primary route—and the primary roadblock—in the pursuit of redress for algorithmic harms. Courts often find algorithmic harms non-cognizable and rarely require developers to address material claims of harm. To address the core challenges of taking algorithms to court, we develop a relational approach to algorithmic accountability that emphasizes not what the actors do nor the results of their actions, but rather how interlocking relationships of accountability are constituted in a triadic relationship between actors, forums, and public(s). As is the case in other regulatory domains, we believe that impact assessments (and similar accountability documentation) can provide the grounds for contestation between these parties, but only when that triad is structured such that the public(s) are able to cohere around shared experiences and interests, contest the outcomes of algorithmic systems that affect their lives, and make demands upon the other parties. Where courts now find algorithmic harms non-cognizable, an impact assessment regime can potentially create procedural rights to protect substantive rights of the public(s). This would require algorithmic accountability policies currently under consideration to provide the public(s) with adequate standing in courts, and opportunities to access and contest the actor's documentation and the forum's judgments.

… to actualizing algorithmic accountability in practice. Here I … resource to do algorithmic-accountability reporting will take … organizations will incorporate the evidence that algorithms or …

The rising use of the artificial intelligence (AI) in the systems of the public has posed a great challenge to the concerns of fairness, accountability, and governance. This paper looks at the concept of algorithmic accountability in three key areas namely the allocation of resources, the handling of the workforce and the safety-related areas of the population. The analysis, based on an integrative qualitative review of chosen scholarly sources, assesses the conceptualisations of accountability mechanisms and their possible application in AI-based decision-making. The results indicate that there is a strong theoretical basis in the current literature, but the application is still small and disparate. Algorithms bias in the allocation systems is also present because of the use of historically (skewed) data and lack of explicit policy frameworks based on equity. When there is low transparency and contestability in the framework of workforce management, it adversely affects institutional trust and procedural fairness. The conflict between speed of making decisions and accountability limits the usefulness of standard accountability frameworks in safety-related scenarios. In all areas, responsibility is often spread among various actors, which create responsibility and implementation loopholes. The research also indicates that transparency is not enough in itself, since in most systems we cannot get readable and actionable information that we can act on to conduct meaningful oversight. The discussion provides the significance of a socio-technical, lifecycle-based approach to accountability, which incorporates both technical design and institutional governance, and regulatory supervision. The best way to tighten the accountability of algorithms is to have more explicit responsibility frameworks, enforceable standards, and increase institutional capacity. The paper comes up with the conclusion that AI-driven public systems should have legitimacy based on the capacity to achieve efficiency, fairness and accountability where technology progress becomes equitable and responsible in governing people.

This article examines how algorithmic mediation reshapes social trust and solidarity in the post-secular age. Historically grounded in shared moral horizons shaped by religion, tradition, and communal practices, trust has increasingly been displaced by technocratic governance, market rationality, and algorithmic systems that mediate work, cognition, communication, and political life. Through a critical analysis of contemporary developments—including algorithmic labour management, neurotechnology, large language models, digital public spheres, technological sovereignty, and global AI governance—the article argues that algorithmic mediation intensifies the fragility of trust by instrumentalizing human agency, fragmenting public reason, and concentrating power within opaque technological infrastructures. Against technological determinism and purely procedural approaches to ethics, the article advances a normative framework rooted in solidarity and the common good. Drawing on post-secular perspectives, a retrieval of natural law normativity, and the resources of Catholic Social Teaching, it contends that trust cannot be sustained through efficiency, prediction, or regulation alone. Instead, social trust depends upon relational goods—dignity, responsibility, participation, and truth—that resist reduction to data-driven optimization. Reclaiming solidarity therefore requires re-embedding AI within moral horizons capable of guiding technological development toward integral human flourishing. In this sense, the governance of AI emerges not merely as a technical challenge but as a decisive moral and political task for post-secular societies.

… , we were able to identify social systems of AIA over time, … , dis- embedding and re- embedding mechanisms are highly … technical capabilities of algorithmic systems facilitate a form of …