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Edited by
Monika Zalnieriute, Law Institute of the Lithuanian Centre for Social Sciences,Agne Limante, Law Institute of the Lithuanian Centre for Social Sciences
International human rights courts and treaty bodies are increasingly turning to automated decision-making (ADM) technologies to expedite and improve their review of individual complaints. These tribunals have yet to consider many of the legal, normative, and practical issues raised by the use of different types of automation technologies for these purposes. This chapter offers an initial assessment of the benefits and challenges of introducing ADM into international human rights adjudication. We weigh up the benefits of introducing these tools to improve international human rights adjudication – which include greater speed and efficiency in processing and sorting cases, identifying patterns in jurisprudence, and enabling judges and staff to focus on more complex responsibilities – against two types of cognitive biases – biases inherent in the datasets on which ADM is trained, and biases arising from interactions between humans and machines. We also introduce a framework for enhancing the accountability of ADM tools that mitigates the potential harms caused by automation technologies in this context.
Ontologies support transparent and reproducible conceptual modeling in Health Technology Assessment (HTA), but their population remains resource-intensive and reliant on expert input. This study evaluates the feasibility, reliability, and methodological implications of using generative artificial intelligence (GenAI) to populate ontology individuals for HTA applications.
Methods
A factorial experimental framework was developed using the Ontology for Simulation Modeling (OSDi) and three HTA-relevant use cases of varying complexity. Two GenAI systems were evaluated under multiple experimental conditions, including prompting strategy, serialization format, and provision of supporting information. Generated ontology individuals were validated by an HTA expert and assessed across four quality dimensions: consistency, relevance, completeness, and adequacy. Multivariate and regression analyses were conducted to examine the effects of experimental factors on quality outcomes and hallucination likelihood.
Results
GenAI systems successfully generated ontology individuals across use cases, although performance varied by quality dimension and experimental condition. Iterative prompting significantly improved completeness, while serialization format strongly influenced reliability, with Turtle serialization associated with substantially lower hallucination likelihood compared with XML. Other factors showed dimension-specific effects, highlighting the multidimensional nature of ontology quality. Errors occurred more frequently in structurally complex ontology components, suggesting a relationship between ontological complexity and generative performance.
Conclusions
GenAI-assisted ontology population can enhance the efficiency and scalability of HTA conceptual modeling, enhancing the agility of HTA agencies in exploratory phases. Its effective use requires structured prompting, appropriate representation formats, and expert validation. Further research should evaluate its impact on HTA decision modeling workflows and validation frameworks.
How does technological change affect social policy preferences? We advance this lively debate by focusing on the role of dual vocational education and training (VET). Existing literature would lead us to expect that dual VET increases demand for compensatory social policy and magnifies the effect of automation risk on such demands. In contrast, we contend that dual VET weakens demand for compensatory social policy through three non-mutually exclusive mechanisms that we refer to as (i) material self-interest; (ii) workplace socialization; and (iii) skill certification. We further hypothesize that dual VET mitigates the association between automation risk and social policy preferences. Analyzing cross-national individual data from the European Social Survey and national-level data on education systems, we find strong evidence for our argument. The paper advances the debate on social policy preferences in the age of automation and sheds new light on the relationship between skill formation and social policy preferences.
Administrative burden describes the learning costs, psychological costs, and compliance costs people face when attempting to interface with the government, particularly in seeking a benefit. Algorithmic and automated processes offer the potential of reducing administrative burdens, but scant empirical research has determined to what, if any effect. This study uses the case of criminal record expungement in two policy contexts: traditional, court petition-based systems and newly enacted automated systems, to understand if and how administrative burden persists, and whether and how these burdens operate differently in the context of the criminal legal system. Drawing on interviews with 105 expungement-eligible people, we find that while automated expungement schemes shift the burden from petitioner to state to initiate the process, automation inadvertently creates new administrative burdens via failure to notify, partial clearances, and opaque data processes. Furthermore, respondents described how automation failed to provide a sense of confirmation from the state that their sentence was truly completed, rehabilitation had been acknowledged, or that collateral consequences should no longer wield the same power. Overall, we argue that leveraging automation to reduce burdens must include information availability by design; otherwise policy reforms may fail to fully achieve their goals.
The exponential growth of scientific literature poses increasing challenges for evidence synthesis. Systematic reviews (SRs) usually rely on keyword-based database searches, which are limited by inconsistent terminology and indexing delays. Citation searching—identifying studies that cite or are cited by known relevant articles—offers a complementary route to uncover additional evidence but remains poorly automated and integrated into screening workflows. We developed BibliZap, an open-source, fully automated citation-searching tool built on Lens.org data, performing multi-level forward and backward citation searches with relevance-based ranking. Its performance was evaluated across 66 published SRs, comparing five approaches: (1) PubMed-only searches; (2) PubMed followed by BibliZap restricted to the top 500 ranked results; (3) PubMed followed by full BibliZap screening; and (4–5) two exploratory early-stop strategies where BibliZap was initiated after identifying the first or the first three PubMed relevant records. The primary outcome was sensitivity, with secondary assessments of screening workload and precision. When used after PubMed screening, BibliZap increased mean sensitivity from 75% to 97%, achieving complete recall in over half of the reviews. Screening only the top 500 outputs still allowed over 90% of reviews to reach or exceed 80% recall. BibliZap recovered a median of three additional included articles per review, not retrieved by PubMed, while adding a median of 6,450 additional records. Citation searching via BibliZap enhances the completeness of evidence retrieval in SRs based on restricted database searches and supports transparent, scalable workflows adaptable to rapid and exploratory review contexts.
Chapter 8 explains why there has been so much enthusiasm for integrating AI into multiple dimensions of the hiring process, from resume screening to interview bots, despite these endeavors being marred by fundamental flaws, including, in some cases, integrating bias, unreliable pseudoscientific methods, and dehumanizing interactions. In addition to analyzing the incentives that have motivated companies to use flawed, innovative tools, we provide a road map for how to develop and use responsible AI upgrades in the hiring process.
We examine long‐run effects of automation risk on turnout. We expect gendered negative effects because men's turnout is more sensitive to job loss and earnings, but negative effects might be offset by populist right‐wing mobilization on economic grievances. We rely on population‐wide administrative data to avoid well‐known biases in survey data. We find both men and women with high automation risk to suffer in the labour market, but automation risk is associated with lower turnout for men only. The negative association with turnout is weaker where the populist right is stronger, consistent with mobilization on economic grievances. Finally, we show experimentally that priming of automation risk produces null findings, suggesting that risks need to have material consequences to affect political behaviour. Our findings imply that technological change has contributed to the emergence of gender gaps in turnout and populist voting as well as the participation drop among the working class.
Accurate radiation dose measurement is crucial for medical intervention and protective actions. Biological dose assessment directly measures radiation-induced molecular and physiological changes, providing information about the absorbed dose and potential health risks. The Korea Institute of Radiological and Medical Sciences (KIRAMS) has performed biological dosimetry using cytogenetic assays since 2010. These assays are used for individual dose estimation in various situations, including occupational exposure, accidental radiation exposure, and health risk assessment of people living near nuclear power plants in Korea. Recent advancements in biological dose assessment methods, such as automated scoring and high-throughput assays, have improved efficiency and enabled more people to undergo dose assessment. The KIRAMS continuously explores new methods and targets for biodosimetry to enhance dose assessment capabilities and can contribute to expand the biological dose assessment capacity with the expertise and facilities, responding to large-scale accidents of radiation exposure in the world.
Chapter 10 predicts the “future” of chilling effects – which today looks darker and more dystopian than ever in light of the proliferation of new forms of artificial intelligence, machine learning, and automation technologies in society. The author here introduces a new term “superveillance” to explain new forms of AI-driven systems of automated legal and social norm enforcement that will likely cause mass societal chilling effects at an unprecedented scale. The author also argues how chilling effects today enable this more oppressive future and proposes a comprehensive law and public policy reforms and solutions to stop it.
This chapter examines some ways in which human agency might be affected by a transition from legal regulation to regulation by AI. To do that, it elucidates an account of agency, distinguishing it from related notions like autonomy, and argues that this account of agency is both philosophically respectable and fits common sense. With that account of agency in hand, the chapter then examines two different ways – one beneficial, one baleful – in which agency might be impacted by regulation by AI, focussing on some agency-related costs and benefits of transforming private law from its current rule-based regulatory form to an AI-enabled form of technological management. It concludes that there are few grounds to be optimistic about the effects of such a transition and good reason to be cautious.
Monsters may frighten but also fascinate us in their weird and unfamiliar ways. As Gramsci once observed, periods of radical transformation are also times of monsters. AI fits the description. It is a bewildering entity, consisting of hard - and software, depending on infrastructures that need huge amounts of energy and water. It defies clear definition, yet seeps into every corner of our lives. Big Tech warns of existential risks while pursuing Artificial General Intelligence, AGI. However, real challenges today lie in how AI threatens to substitute rather than augment human capabilities.
This essay examines the deployment of an AI-based interdisciplinary approach. It has proven spectacularly successful, as exemplified by AlphaFold2's breakthrough in protein folding. This approach operates frictionlessly, combining knowledge domains with remarkable efficiency and speed. It seems to vindicate a technocratic dream of problem-solving without the messiness and time needed for human deliberation. Yet, when this artificial interdisciplinarity enters the social world, it encounters what it seeks to eliminate: friction.
Friction, however, is not an obstacle to overcome but an essential feature of human existence. The physical world requires friction for movement; the social world needs it for creativity, conflict resolution, and meaningful cooperation. Certainly, too much friction can bring havoc, and too little can lead to a standstill. But as AI continues its co-evolutionary trajectory with humanity, we must resist the seductive promise of a frictionless world run by automated efficiency.
Instead, we need to cultivate a humanistic culture of AI interdisciplinarity - one that bridges sciences and humanities while preserving human curiosity, deliberation, and epistemic diversity. Bringing friction back means taking the time to reconsider shared goals, acknowledging conflicts, and maintaining spaces for genuine human creativity. Only by embracing friction can we ensure that AI augments rather than diminishes what makes us human.
Radical political economy focuses on capitalism's ability for reproduction. Social reproduction refers to how human beings reproduce their existence. Globalization has seen a vast expansion of surplus labor or surplus humanity. The levels of worldwide inequalities are unprecedented, as is the extent of mass deprivation and precarity. Transnational capital has turned to new forms of unpaid labor to expand accumulation, helping to generate a worldwide crisis in gender relations. A new round of global enclosures is underway that includes land grabs around the world. The TCC is turning toward greater automation in both the traditional core and the traditional periphery, suggesting an increase in the production of relative surplus value relative to absolute surplus value. The global mining industry, and the case of the Congo, illustrate these transformations. As artificial intelligence spreads, professional work and knowledge workers also face deskilling, automation, and increased precariousness. Capitalist states could ameliorate the crisis through redistribution and regulatory policies, but they are constrained by the structural power of transnational capital.
The ability to modify designs, personalize nutrition, and improve food sustainability makes 3D food printing (3DFP) an exciting emerging technology. Food materials’ complex chemistry and mechanics make it difficult to consistently print designs of different shapes. This research uses two methods to assess printed food fidelity: Manual and automated image analysis with custom-developed algorithm. Fidelity based on printed area was measured for three overhang designs (0°, 30°, and 60°) and three food ink mixtures. The manual method provided a baseline for analysis by comparing printed images with CAD images. Both methods showed consistent results with only ±3% differences in analyzing printed design areas. While the computational method offers advantages for efficiency and bias reduction, making it well-suited for fidelity assessment to assess designs.
The offshoring-fuelled growth of the Central and Eastern European business services sector gave rise to shared service centres (SSCs) – quasi-autonomous entities providing routine-intensive tasks for the central organisation. The advent of technologies such as intelligent process automation, robotic process automation, and artificial intelligence jeopardises SSCs’ employment model, necessitating workers’ skills adaptation. The study challenges the deskilling hypothesis and reveals that automation in the Polish SSCs is conducive to upskilling and worker autonomy. Drawing on 31 in-depth interviews, we highlight the negotiated nature of automation processes shaped by interactions between headquarters, SSCs, and their workers. Workers actively participated in automation processes, eliminating the most mundane tasks. This resulted in upskilling, higher job satisfaction, and empowerment. Yet, this phenomenon heavily depends upon the fact that automation is triggered by labour shortages, which limit the expansion of SSCs. This situation encourages companies to leverage the specific expertise entrenched in their existing workforce. The study underscores the importance of fostering employee-driven automation and upskilling initiatives for overall job satisfaction and quality.
Having looked at how firms develop innovations and bring them to market, and the role of entrepreneurs and states in shaping those processes, we turn now to the question of what innovations do to society. Innovations, after all, do not just concern the firms that create them. We begin at the most macro of macroscopic levels with Perez’s paper on technology bubbles, asking how societies are transformed through successive waves of technological revolution and what happens as those waves flood over society. Staying at the macroscopic perspective with Zuboff’s paper on Big Other, we look at how technological change transforms capitalist dynamics and ushers in both new logics of accumulation and new forms of exploitation. Then, we move to the question that the popular press tends to phrase as “Will robots take our jobs?” as we look at the history and future of workplace automation with Autor’s paper and Bessen’s analysis of the conditions that lead to widespread, as opposed to highly concentrated, societal gains from technology.
The chapter explores the implications of the growing integration of AI and robotics into the workforce, questioning whether it will lead to a golden age or a dystopian era. It emphasizes the transformative impact of these technologies on various industries, with robots taking over tasks from manufacturing and distribution to healthcare and caregiving. The benefits include increased productivity, efficiency, safety, flexibility, cost-savings, improved precision, and enhanced quality of life. However, concerns arise regarding potential job displacement and loss, particularly for low-skilled and low-paid workers. The passage discusses the likelihood of robots replacing up to half of all jobs in the coming decades, posing challenges for reemployment and skills adaptation. The issue of inequality is raised, highlighting that low-skilled workers may be disproportionately affected. The chapter also touches on the societal disruptions, identity crisis, and potential resistance that could emerge due to widespread automation. Various policy prescriptions are proposed to address these challenges, including investing in education, training, and reskilling, implementing a universal basic income (UBI), guaranteeing jobs, exploring job-sharing and reduced hours, and considering a tax on robot labor.
Commercial cattle slaughter operations have shown an increasing trend towards automation, with the aim being to improve animal welfare, product quality and efficiency. Several cattle slaughter plants have introduced mechanical rump pushers (RP) prior to the entrance of the stun box to reduce human-animal interaction and facilitate a smoother transition from the raceway to stun box. Presently, there are no data regarding the use of RPs in commercial slaughter environments operating at 40 cattle per hour. Therefore, this study observed normal operations at a UK slaughter plant, which has an RP installed, and assessed the level of coercion required to enter the RP, the use of the RP, cattle behaviour inside the RP and carcase bruising. The RP was used on 267 of the 815 cattle observed (32.8%) and was more likely to be used on dairy cattle and those who received a higher coercion score when entering the RP. Overall, 60 cattle (7.4%) required the highest coercion score and four (0.49%) required the use of the electric goad. Inside the RP, eleven animals slipped (1.8%) and ten vocalised (1.6%) although no incidences were directly associated with RP use. However, increased time restrained in the RP was significantly associated with more gate slams into the RP entrance gate. The use of the RP was not significantly associated with carcase bruising. These results are encouraging, and although it cannot be concluded that the presence of an RP improves cattle welfare at slaughter, use of automation within cattle slaughter facilities warrants further investigation.
This chapter addresses the implications of the 100-year-life for the future of work and the law of work. To begin with, longer lives will pose severe actuarial challenges to all existing strategies for ensuring retirement income security. At least without a dramatic (and probably unjustified) shift of social welfare expenditures into support of nonworking seniors, most people will probably have to work longer, if they are healthy and able, to generate enough income for retirement. The chapter then turns to how the law of work might have to change to accommodate longer working lives. Leaving aside the law of age discrimination (addressed in another chapter), longer working lives will recast longstanding debates over job security and will highlight the need to make work and work schedules less demanding, especially as workers age. This chapter will explore these challenges and how demographic changes will intersect with changing technology and its impact on the nature and number of jobs.
This study examines the association between firm-level investments in automation technologies and employment outcomes, drawing on a panel dataset of approximately 10,450 Italian firms. We focus on the proliferation of non-standard labour contracts introduced by labour market reforms in the 2000s, which facilitated external labour flexibility. Our findings reveal a positive relationship between automation investments and the adoption of these flexible labour arrangements. Guided by a conceptual framework, we interpret this result as evidence of complementarity between automation technologies – viewed as flexible capital – and non-standard contractual arrangements – viewed as flexible labour. This complementarity is essential for enhancing operational flexibility, a critical driver of firm performance in competitive market environments. From a policy perspective, our analysis highlights the importance of measures that protect labour without undermining the efficiency gains enabled by automation.