artificial intelligence

Systematic Evaluation of a Transformer-Based Surrogate for Distributed Groundwater Dynamics: Long-Horizon Prediction, Dynamic–Static Fusion, and Data Augmentation

Transformers are increasingly reshaping artificial-intelligence-based Earth system prediction, yet their suitability for distributed groundwater surrogate modeling remains poorly understood. Groundwater dynamics combine strong temporal persistence with spatial interactions governed by heterogeneous hydrogeological conditions. These characteristics make attention-based architectures potentially well suited to representing the long-range spatiotemporal dependencies of groundwater systems, rather than merely providing an alternative to recurrent models. Here, we systematically evaluated a groundwater-adapted PredFormer as a surrogate for regional ParFlow-CLM simulations. The adapted model combined temporal-first factorized attention with pressure-residual prediction and blockwise autoregressive rollout. We assessed its long-horizon prediction behavior over 720 h, the contributions of prescribed P−ET (PME) forcing and static attributes, alternative static-fusion strategies, its performance relative to PredRNN, and precipitation-regime-specific data augmentation generated by perturbing precipitation forcing. Over the horizon, the adapted PredFormer maintained decimeter-scale median water table depth (WTD) errors, although error magnitude varied among WTD regimes. Larger upper-tail spatial errors were associated with precipitation amount and variability, whereas temporal fluctuations in the error trajectories were more closely associated with abrupt wet–dry transitions. Adding prescribed PME produced the largest and most consistent reduction in prediction error, although the benefits of dynamic and static inputs varied systematically with WTD. Post-temporal–spatial cross-attention performed best among the static-fusion strategies. PredRNN was more accurate initially, but PredFormer performed better at longer lead times and in most segments. Data augmentation produced modest overall changes but clear regime-dependent benefits when the added samples matched the precipitation conditions encountered during prediction. These findings show that the advantages of Transformer-based groundwater surrogates emerge primarily over extended autoregressive horizons and depend on groundwater state, input composition, information-fusion strategy, and training-sample coverage. The results provide process-informed guidance for developing computationally efficient groundwater surrogates for regional prediction and water-resources management, while also highlighting their longer-term potential to provide AI-enabled representations of groundwater dynamics within Earth system models.

Hole in the Loop: When Common-Mode Failure of Human Oversight Appears as Successful Oversight

Human-in-the-loop (HITL) architectures retain human verification in artificial intelligence (AI)-assisted decisions to preserve safety. Human presence does not guarantee AI-independent judgment. Multiple substantive checks can share one AI-dependent information pathway, and a Reciprocal Validation Spiral can reinforce that dependency when human approval becomes evidence for further AI trust and delegation. This paper treats independence as graded and defines a Hole in the Loop at a decision or verification point when formal HITL remains intact and zero substantive verification pathways meet a domain-specific minimum criterion for effective independence. Before such a threshold is operationalized, a limiting case is identifiable: every substantive check is downstream of the same AI output or AI-conditioned judgment, and no checker forms a pre-AI judgment or consults AI-independent evidence. The Spiral can generate, deepen, or stabilize a Hole, while a Hole can also exist without it. When the common AI-dependent pathway is systematically wrong, no effectively independent route remains to register the discrepancy before downstream action. The error can pass with multiple approvals and a completed audit trail while agreement rises, override rates fall, and procedural compliance appears complete. Correlated agreement can therefore be recorded as independent confirmation. The paper argues that calling oversight of a high-risk system meaningful carries an ethical obligation to preserve effectively independent verifiability at decision points that matter and to make its dependency structure auditable. The relevant unit of AI safety therefore moves from human presence to the preservation and observability of effectively independent verification pathways.

Heim's Syntrometric Logic: A Modernized Exposition

AbstractThis repository contains a modernized exposition and formal mathematical reconstruction of Burkhard Heim’s Syntrometrische-Maximentelezentrik, one of the principal theoretical works within Heim’s broader mathematical framework.The work develops a highly specialized formal system combining logic, semantics, and structural mathematics. This exposition reconstructs Heim’s syntrometric logic using contemporary mathematical language, notation, and formal methods, while seeking to preserve the essential structure and conceptual intent of the original framework. Where appropriate, Heim’s terminology and constructions are clarified, formalized, and related to established concepts in mathematical logic, category theory, mathematics, and theoretical computer science.A central objective of the reconstruction is to make Heim’s formal system intelligible and analyzable within a contemporary mathematical setting without presenting the modernization as part of Heim’s original formulation. The exposition therefore distinguishes between historical source material, formal reconstruction, and newly developed mathematical interpretations.The book also investigates potential applications of the reconstructed framework to cognitive modeling and artificial intelligence. These applications are presented explicitly as formal extensions and computational interpretations of the reconstructed logical system, rather than as claims about Heim’s original work.The repository is intended as a scholarly resource for researchers interested in formal logic, modal logic, mathematical foundations, category theory, information theory, systems theory, theoretical computer science, cognitive science, philosophy of science, and Burkhard Heim’s theoretical work.Source MaterialThis work was developed from archival manuscripts and publicly available source material associated with the Heim Theory Archive. Its objective is to provide a rigorous contemporary exposition and formal reconstruction of Heim’s syntrometric logic while maintaining fidelity to the mathematical structure and conceptual intent of the original work.The reconstruction does not assume that modern terminology or mathematical formalisms were explicitly present in Heim’s original formulation. Instead, such formalisms are introduced where they provide a precise contemporary representation, clarification, or extension of structures found in the source material.Copyright and AttributionThis work is an independent scholarly exposition and formal reconstruction prepared for educational, research, and archival purposes. It is not an official or authorized publication of Burkhard Heim’s works.Copyright in the original German works of Burkhard Heim remains with the respective rights holders, including Burkhard Heim’s estate and/or the original publishers, where applicable.This repository does not claim ownership of the original works or their underlying intellectual contributions. Burkhard Heim remains the original author of Syntrometrische-Maximentelezentrik. The mathematical reconstructions, expository material, formalizations, and contemporary interpretations presented here constitute independent scholarly work.When referencing this repository, readers should distinguish clearly between Heim’s original formulations and the modernized reconstructions or extensions developed in this work.CitationWhen referencing this work, please acknowledge:Burkhard Heim — original author of Syntrometrische-Maximentelezentrik.This modernized exposition and formal reconstruction — as the contemporary work consulted or cited.

HACCP for AI: An Auditable Self-Control Standard for Artificial Intelligence

Almost every serious instrument in AI governance now requires an organisation to run a risk process. None of them tells the organisation where in its own lifecycle the control points sit, what measurable limit applies at each one, what happens automatically when a limit is breached, or who is competent to verify the measurement. That layer, process control, is missing, and it is the layer on which everything else depends. A management system without control points produces documentation; it does not produce safety. This paper supplies that layer by importing a method that has governed an invisible hazard across a globally distributed production chain for more than fifty years: Hazard Analysis and Critical Control Points. The claim is deliberately narrow. HACCP is not proposed as a rival to ISO/IEC 42001, to Article 9 of the EU AI Act, or to the NIST AI Risk Management Framework. It is proposed as the process-control layer their architecture presupposes and does not contain, and as the one methodology in existence with a property AI governance urgently needs and currently lacks: scale invariance. The same seven principles govern a village bakery and a multinational dairy, which is why one inspector, one standard and one accreditation system can cover both. The method is given in the full twelve-step Codex sequence, with a three-tier proportionality rule (deployer, provider, frontier developer), a hazard taxonomy, a control-point decision tree, a catalogue of seven control points, and a taxonomy of limits that separates critical limits from operational limits and indicator thresholds, and reclassifies the compute thresholds now written into law as the third kind. Version 5.0 adds what an expert reader of version 4.0 was entitled to ask for: the instruments. A plan that says “monitor the violation rate” has not yet said anything an engineer can implement. Part II therefore imports, with their arithmetic, the measurement tools that make food-safety limits real: attribute sampling plans that state how many outputs to inspect and how many failures to tolerate before a lot is rejected, with their operating characteristics; statistical process control that gives the operational limit a precise meaning as a warning limit on a control chart; a seven-step protocol for validating an evaluation as a measuring instrument, with inter-rater reliability, uncertainty and a scope statement; and record schemas (a configuration manifest, a monitoring record, a deviation register) that make version identity and traceability a matter of implementation rather than intention. Part III applies the instruments in four worked plans, including a new one for an agent with tool access, and specifies a pre-registrable inter-rater study by which the method's central claim to auditability can be tested and, if warranted, refuted. The paper states its own limits without softening. The most consequential gap in AI-governance infrastructure is metrological: there is no analogue of ISO/IEC 17025 for capability evaluation, and until there is, independent attestation of a capability-based limit is not available to anyone. One structural disanalogy has no food-safety precedent at all: a pathogen does not model the control system trying to detect it, and a sufficiently capable AI system may. Both are treated as design constraints on the method rather than as objections to be answered later. The framework is accordingly strongest where most AI harm occurs, in organisations that deploy and provide AI systems, and is stated to be aspirational at the frontier until independent measurement exists. This record contains three files: the full paper (version 5.0, 73 pages), a two-page Executive Summary for policy readers, and a 15-page Practitioner Brief containing Part II (the instruments) as a standalone document. Version 5.0 supersedes version 4.0 (July 2026); earlier versions remain available under the same concept DOI. CC BY 4.0.

AI dependency and its relationships with anxiety, quality of life, and digital stress

Abstract Background The integration of artificial intelligence (AI) into higher education has accelerated, yet little is known about the psychological mechanisms underlying students’ reliance on AI. This study conceptualizes AI dependency as a complex cognitive–motivational construct that extends beyond mere usage, influencing anxiety, digital stress, and quality of life. Methods A 521 participants (predominantly undergraduate, 80% female) recruited via snowball sampling at King Abdulaziz University. Self-administered standardized instruments assessed AI dependency, AI-related general anxiety, digital stress, quality of life, and AI dependency. A network analysis approach was employed to examine the interrelations among AI dependency, cognitive offloading, anxiety, availability pressure, FoMO, digital overload, digital vigilance, social acceptance anxiety, and quality of life among university students. This approach allowed identification of central variables and conditional interactions within a dynamic psychological system. Results AI dependency emerged as a structurally foundational variable, reorganizing students’ cognitive and emotional experiences. It was closely linked to digital stressors, including digital vigilance and fear of missing out, while anxiety functioned as a mediator connecting cognitive reliance to environmental pressures. Social acceptance anxiety translated cognitive pressures into relational–identity concerns, and cumulative effects manifested in reduced quality of life. The network revealed non-linear, conditional associations, highlighting that the psychological impact of AI dependency is mediated by cognitive, motivational, and contextual factors rather than by direct usage intensity alone. Conclusions AI dependency is not a neutral or purely functional behavior but a central psychological construct with both potential advantages, such as reduced cognitive load and increased efficiency, and risks, including diminished autonomy, heightened anxiety, and long-term digital strain. These findings offer a culturally contextualized model for understanding AI’s influence on student well-being and provide a framework for interventions that target central nodes in the network to promote healthier engagement with AI in academic settings.

Data Set For Artificial Intelligence and Multicultural Religious Education: A Systematic Review of Ethical Mediation and Decolonial Practices

This dataset comprises the complete supplementary materials for a decolonial qualitative systematic review examining how Artificial Intelligence (AI) mediates Multicultural Religious Education (MRE) across Islamic, Christian, Jewish, Hindu, Buddhist, and indigenous educational traditions. The review synthesizes 254 peer-reviewed studies (2014–2026) from English, Indonesian, and Arabic sources, following PRISMA 2020 guidelines. The core contribution is an empirically-grounded framework identifying six AI mediation mechanisms along a continuum from algorithmic substitution (moral outsourcing, normative amplification, cultural flattening) to dialogic integration (hybrid deliberative mediation, contextual value re-embedding, dialogic ethical framing). The analysis further documents five decolonial possibilities emerging from Global South practices: epistemic re-centering, pedagogical sovereignty, cultural counter-flattening, governance innovation, and transnational solidarity. All supplementary materials supporting the main article are provided here, including complete study characteristics, inter-coder agreement matrices, quality appraisals, thematic analyses, and detailed appendices with operational definitions and conceptual frameworks. Supplementary Material Description SM-01 Study Characteristics Mapping – Complete bibliographic data, context, and methodology for all 254 studies SM-02 Inter-Coder Agreement Matrix – Theme-level Fleiss' κ scores demonstrating reliability (mean κ = 0.87) SM-03 JBI Critical Appraisal – Quality assessment scores with strengths and limitations for each study SM-04 PICo and Thematic-Content Analysis Matrix – Population-Interest-Context mapping with operational definitions SM-05 Theme-SWOT-CIMO Analysis – SWOT categorization with dominant mechanisms for all studies SM-06 Distribution of Reviewed Studies – Geographic, methodological, and educational level distribution (N=254) SM-07 Condensed Evidence Matrix – PICo × DEAM-MRE Domain × Region × Mechanism (N=254) SM-08 Mechanism Density Analysis – CIMO framework distribution across the corpus SM-09 Intercoder Reliability Summary – Derived from SM-02 with domain-level aggregation SM-10 MASTER DATA MATRIX SM-11 SECOND-CODER VERIFICATION REPORT SM-12 VALIDATION, RECONCILIATION SM-13 All References

FERIAL ZAYED THE WORLD'S FIRST OPEN-SOURCE DIAGNOSTIC ARTIFICIAL INTELLIGENCE FOR HUMANITY A Production-Ready Architecture for Global Health Integration

FERIAL ZAYED THE WORLD'S FIRST OPEN-SOURCE DIAGNOSTIC ARTIFICIAL INTELLIGENCE FOR HUMANITY A Production-Ready Architecture for Global Health Integration

FERIAL ZAYED THE WORLD'S FIRST OPEN-SOURCE DIAGNOSTIC ARTIFICIAL INTELLIGENCE FOR HUMANITY A Production-Ready Architecture for Global Health Integration

FERIAL ZAYED THE WORLD'S FIRST OPEN-SOURCE DIAGNOSTIC ARTIFICIAL INTELLIGENCE FOR HUMANITY A Production-Ready Architecture for Global Health Integration

Generative Artificial Intelligence: Ethical challenges and pedagogical implications in emerging educational contexts

Generative Artificial Intelligence (GAI) has rapidly transformed higher education by introducing new possibilities for personalized learning and pedagogical innovation. It has emerged as a powerful support system capable of information processing and decision-making tasks in higher education. Unlike traditional educational technologies, models such as ChatGPT enable dynamic interaction with learners. Students engage with knowledge, complete academic tasks, and regulate their performance. Building upon previous studies, this article incorporates an expanded theorical framework, and a deeper ethical analysis of student perceptions and emotions, emphasizing its role as a support tool in their learning processes. The results suggest that while ChatGPT enhances efficiency, accessibility, and learner autonomy, it also introduces significant risks related to overreliance, academic integrity, and the loss of critical thinking skills. The study concludes that should be integrated as an academic mediator, emphasizing the need for ethical guidelines and pedagogical institutional redesign.   Spanish-language metadata / Metadatos en españolTítulo en español:Inteligencia Artificial Generativa: desafíos éticos e implicaciones pedagógicas en contextos educativos emergentes Resumen:La Inteligencia Artificial Generativa (IAG) ha transformado rápidamente la educación superior al introducir nuevas posibilidades para el aprendizaje personalizado y la innovación pedagógica. Se ha consolidado como un potente sistema de apoyo capaz de realizar tareas de procesamiento de información y toma de decisiones en el ámbito de la educación superior. A diferencia de las tecnologías educativas tradicionales, modelos como ChatGPT permiten una interacción dinámica con los estudiantes. Estos interactúan con el conocimiento, realizan tareas académicas y regulan su propio desempeño. A partir de estudios previos, este artículo incorpora un marco teórico ampliado y un análisis ético más profundo de las percepciones y emociones de los estudiantes, haciendo énfasis en el papel de la IAG como herramienta de apoyo en sus procesos de aprendizaje. Los resultados sugieren que, aunque ChatGPT mejora la eficiencia, la accesibilidad y la autonomía del estudiante, también introduce riesgos significativos relacionados con la dependencia excesiva, la integridad académica y la pérdida de habilidades de pensamiento crítico. El estudio concluye que la IAG debería integrarse como mediadora académica, destacando la necesidad de establecer directrices éticas y de llevar a cabo un rediseño pedagógico e institucional. Palabras Claves:Inteligencia Artificial Generativa, entorno educativo, procesos de aprendizaje Smart citations: SciteAI. Dimensions.Open Alex.

The ethics of refusal: Why the Indigenous ‘no’ matters in the age of artificial intelligence

ENACT Analytical Report 09 - Methods to facilitate the traceback and mitigate the spoofing of telecommunications

Executive Smmary The tracing of communications is used to fight many kinds of crime, but its effectiveness depends on the resources that law enforcement agencies (LEAs) can dedicate to acting upon intelligence supplied by communication providers (CPs). China has extradited many thousands of scammers from overseas call centres despite not asking foreign countries to build new systems to facilitate traceback. The USA’s strategy concentrates on technology, including heavy expenditure on systems that facilitate traceback. However, the resulting number of prosecutions has been modest despite large volumes of scam calls originating on major US networks and a continued rise in the amounts lost by American consumers to scams instigated by a phone call. Europe can draw lessons from the different approaches adopted by China and the USA, including their respective strengths and limitations. In turn, Europe has much to teach the rest of the world about its own successes in tackling scams. There is some ambiguity in what different parties mean when they discuss traceback. This leads to confusion about what is required of a traceback system. Solutions are designed incorrectly if there is no clear statement of requirements. Several respondents to our survey noted that traceback essentially involves asking CPs where a communication came from, per the records they retained. Countries have been tracing communications for many years. So, when considering how to implement a new system, we should ask what we are trying to trace, where we expect to trace it to, and what is deficient with the systems to be replaced. The notion that a global traceback system stems from work on an allied problem: the authentication of communications.‘Authentication’ is a way of saying work has been done to prevent impersonation. Much of this work is imperfect. This report links the questions of how to tackle spoofing with how to trace communications. Tracing is more general in application. And if we know the origin of a communication with certainty, because it has been authenticated and the sender was subject to rigorous know-your-customer (KYC) checks, then ‘tracing’ that communication is just a peculiar way of saying we look at the sender’s address per the communication. Chapter 5 of this report evaluates 15 methods for authentication or tracing (or both). Considering all the options helps to clarify the strengths and weaknesses of each one. The effectiveness of traceback methods based on hop-by-hop tracing may be limited by criminal adaptation and cross-border implementation challenges. Hop-by-hop tracing is easy to defeat on a technical level because criminals will find alternate routes for their traffic to prevent it being traced. They have decades of experience of routing international communications traffic to evade detection. Hop-by-hop tracing can also be obstructed by laws that prevent the international exchange of relevant data. Even if all countries could be persuaded to support a global hop-by-hop method of tracing dialled voice calls, it would still be rendered redundant before it entered service because criminals would stop using dialled voice calls and start using other ways to transmit their voice.Phone users are switching to over the top services that allow them to speak and send messages to friends, family and businesses without dialling telephone numbers. So are scammers. We should be clear about whether we want to speed up the way tracing has been done in the past – by making enquiries of each CP in turn – or if LEAs want to identify the sources of crime as directly as possible. Transmitting tracing and authentication data in-band within telecoms networks means it follows the same route as the communication, and hence incurs the risk of being interrupted at any hop. Transmitting data out of band avoids those risks because it only requires cooperation between the authorities and CPs at the origin and destination of the communication. Out of band methods are less mature at present but have a more realistic prospect of eventual success. Some individual nations are setting up out of band data exchanges between their telcos without trying to standardise for international cooperation. Meanwhile, the limitations identified in current STIR/SHAKEN implementations have prompted renewed interest in alternative approaches, including out of band SHAKEN and Open Verified Communication. The EU has an opportunity to become a technological leader in this domain by connecting the threads of this work to its eIDAS roadmap for digital ID. European LEAs could look to the future by seeking general-purpose authentication and tracing systems that can also be applied to the newer generations of services that deliver voice and messaging communications through over-the-top and hybrid routes. This would involve out of band transfers of authentication and traceback data so the flow of data is independent of the specifics of the communication protocols implemented by the private sector. The benefit would be that a future-proofed tracing and authentication framework would ignore the increasingly artificial distinction between a voice call instigated using a dialled number or an app, or between text messages sent via SMS, RCS, WhatsApp and iMessage. For all the focus on novel network technologies, it is easy to overlook the fact that Europe is already a world leader in tackling spoofing. The widespread adoption of ECC Recommendation (23)03 has greatly reduced the number of inbound international calls that spoof the domestic phone numbers of Europeans.Where figures are available, it is not unusual to see this control being credited for reductions in scam calls of around 70 or 80 percent. Its effectiveness greatly alters the cost-benefit argument for other controls that require different countries to cooperate. Sender ID registries are similarly effective at reducing the most common text message scams. Some of the most effective anti-scam methods covered by this report have been put into effect in countries that have advanced anti-scam policies, such as Singapore and Australia. But Europe does not need to look to other continents for leadership. Ireland provides an example of a national strategy that has combined relatively simple measures with reported reductions in telecom-related scams. The EU is well-placed to coordinate efforts to tackle networked crime, just as it led the telecoms and tech industries through the adoption of the GSM standards for mobile telephony, the introduction of mobile roaming, and the data privacy rules enshrined in GDPR. ECC Recommendation (23)03 is an example of recent European leadership in consumer protection; eIDAS establishes a positive future for digital ID that serves the needs of EU citizens. By first concentrating on methods that obstruct the flow of scam communications, and then implementing bilateral exchanges of data with countries that want to aid the prosecution of criminals they harbour, Europe can make the most tangible gains in reducing scams. Europe can do this by favouring the authentication and tracing technologies that already prioritise the goal of consumer protection while respecting European standards for security and privacy. This report was prepared by the External Expert Erik Priezkalns, while the topic of the report was requested by Europol’s European Cybercrime Centre (EC3) to analyse existing and operational technical solutions to mitigate Caller-ID and Sender-ID spoofing. We’re collecting feedback on this report through the EU Survey Platform, if you’d like to share your thoughts please click on the link below. https://ec.europa.eu/eusurvey/runner/enact-report-feedback

ENACT Analytical Report 09 - Methods to facilitate the traceback and mitigate the spoofing of telecommunications

Executive Smmary The tracing of communications is used to fight many kinds of crime, but its effectiveness depends on the resources that law enforcement agencies (LEAs) can dedicate to acting upon intelligence supplied by communication providers (CPs). China has extradited many thousands of scammers from overseas call centres despite not asking foreign countries to build new systems to facilitate traceback. The USA’s strategy concentrates on technology, including heavy expenditure on systems that facilitate traceback. However, the resulting number of prosecutions has been modest despite large volumes of scam calls originating on major US networks and a continued rise in the amounts lost by American consumers to scams instigated by a phone call. Europe can draw lessons from the different approaches adopted by China and the USA, including their respective strengths and limitations. In turn, Europe has much to teach the rest of the world about its own successes in tackling scams. There is some ambiguity in what different parties mean when they discuss traceback. This leads to confusion about what is required of a traceback system. Solutions are designed incorrectly if there is no clear statement of requirements. Several respondents to our survey noted that traceback essentially involves asking CPs where a communication came from, per the records they retained. Countries have been tracing communications for many years. So, when considering how to implement a new system, we should ask what we are trying to trace, where we expect to trace it to, and what is deficient with the systems to be replaced. The notion that a global traceback system stems from work on an allied problem: the authentication of communications.‘Authentication’ is a way of saying work has been done to prevent impersonation. Much of this work is imperfect. This report links the questions of how to tackle spoofing with how to trace communications. Tracing is more general in application. And if we know the origin of a communication with certainty, because it has been authenticated and the sender was subject to rigorous know-your-customer (KYC) checks, then ‘tracing’ that communication is just a peculiar way of saying we look at the sender’s address per the communication. Chapter 5 of this report evaluates 15 methods for authentication or tracing (or both). Considering all the options helps to clarify the strengths and weaknesses of each one. The effectiveness of traceback methods based on hop-by-hop tracing may be limited by criminal adaptation and cross-border implementation challenges. Hop-by-hop tracing is easy to defeat on a technical level because criminals will find alternate routes for their traffic to prevent it being traced. They have decades of experience of routing international communications traffic to evade detection. Hop-by-hop tracing can also be obstructed by laws that prevent the international exchange of relevant data. Even if all countries could be persuaded to support a global hop-by-hop method of tracing dialled voice calls, it would still be rendered redundant before it entered service because criminals would stop using dialled voice calls and start using other ways to transmit their voice.Phone users are switching to over the top services that allow them to speak and send messages to friends, family and businesses without dialling telephone numbers. So are scammers. We should be clear about whether we want to speed up the way tracing has been done in the past – by making enquiries of each CP in turn – or if LEAs want to identify the sources of crime as directly as possible. Transmitting tracing and authentication data in-band within telecoms networks means it follows the same route as the communication, and hence incurs the risk of being interrupted at any hop. Transmitting data out of band avoids those risks because it only requires cooperation between the authorities and CPs at the origin and destination of the communication. Out of band methods are less mature at present but have a more realistic prospect of eventual success. Some individual nations are setting up out of band data exchanges between their telcos without trying to standardise for international cooperation. Meanwhile, the limitations identified in current STIR/SHAKEN implementations have prompted renewed interest in alternative approaches, including out of band SHAKEN and Open Verified Communication. The EU has an opportunity to become a technological leader in this domain by connecting the threads of this work to its eIDAS roadmap for digital ID. European LEAs could look to the future by seeking general-purpose authentication and tracing systems that can also be applied to the newer generations of services that deliver voice and messaging communications through over-the-top and hybrid routes. This would involve out of band transfers of authentication and traceback data so the flow of data is independent of the specifics of the communication protocols implemented by the private sector. The benefit would be that a future-proofed tracing and authentication framework would ignore the increasingly artificial distinction between a voice call instigated using a dialled number or an app, or between text messages sent via SMS, RCS, WhatsApp and iMessage. For all the focus on novel network technologies, it is easy to overlook the fact that Europe is already a world leader in tackling spoofing. The widespread adoption of ECC Recommendation (23)03 has greatly reduced the number of inbound international calls that spoof the domestic phone numbers of Europeans.Where figures are available, it is not unusual to see this control being credited for reductions in scam calls of around 70 or 80 percent. Its effectiveness greatly alters the cost-benefit argument for other controls that require different countries to cooperate. Sender ID registries are similarly effective at reducing the most common text message scams. Some of the most effective anti-scam methods covered by this report have been put into effect in countries that have advanced anti-scam policies, such as Singapore and Australia. But Europe does not need to look to other continents for leadership. Ireland provides an example of a national strategy that has combined relatively simple measures with reported reductions in telecom-related scams. The EU is well-placed to coordinate efforts to tackle networked crime, just as it led the telecoms and tech industries through the adoption of the GSM standards for mobile telephony, the introduction of mobile roaming, and the data privacy rules enshrined in GDPR. ECC Recommendation (23)03 is an example of recent European leadership in consumer protection; eIDAS establishes a positive future for digital ID that serves the needs of EU citizens. By first concentrating on methods that obstruct the flow of scam communications, and then implementing bilateral exchanges of data with countries that want to aid the prosecution of criminals they harbour, Europe can make the most tangible gains in reducing scams. Europe can do this by favouring the authentication and tracing technologies that already prioritise the goal of consumer protection while respecting European standards for security and privacy. This report was prepared by the External Expert Erik Priezkalns, while the topic of the report was requested by Europol’s European Cybercrime Centre (EC3) to analyse existing and operational technical solutions to mitigate Caller-ID and Sender-ID spoofing. We’re collecting feedback on this report through the EU Survey Platform, if you’d like to share your thoughts please click on the link below. https://ec.europa.eu/eusurvey/runner/enact-report-feedback