Journal of Ethics in Emerging Technologies & Society p-ISSN 3117-5996 and e-ISSN 3117-6003
Journal Insights Editor-In-Chief: Dr. Sergey Garchenko Title proper: Journal of Ethics in Emerging Technologies & Society e-ISSN: 3117-6003 p-ISSN: 3117-5996 Subject: Science and knowledge in general. Organization of intellectual work. Publisher: GalaxiaUniverse Dates of publication: 2024 Frequency: Quarterly Language: English Country: Netherlands Medium: Electronic Version Open Access: Yes Licensing: CC BY-NC-ND license Title DOI of the Journal: https://doi.org/ ORCiD ID: https://orcid.org/ Contact: editor@galaxiauniverse.com Important Tools: English Grammar Checker Plagiarism Checking Proofreading Publishing Timeline: |Time to First Decision: 1 Month| Time to Final Decision: 2 Months| Publication Time: 2 Months| Acceptance Rate: 38% Journal Summary: Journal of Ethics in Emerging Technologies & Society (JEETS) The Journal of Ethics in Emerging Technologies & Society (JEETS) is an academic platform dedicated to exploring the ethical implications, governance, and societal impact of emerging and transformative technologies. It publishes rigorous scholarly discussions on developments in technologies such as genetic engineering, advanced pharmacology, artificial intelligence, nanotechnology, robotics, and related fields. JEETS focuses on ethical issues like bio/moral enhancement, technology governance, risk assessment, robotics and employment, artificial persons (e.g., sentient robots), and personal identity enhancements. The journal emphasizes anticipatory ethics to understand and guide the responsible development and deployment of cutting-edge technologies. Articles are published under an open access model, with creative commons licensing that allows broad dissemination and reuse. JEETS maintains high editorial and publication ethics standards including rigorous peer review, originality checks, transparency, and correction of the scientific record. The journal serves interdisciplinary audiences including ethicists, technologists, policymakers, social scientists, and practitioners concerned with the ethical dimensions of technology's future impact on society. This summary reflects the journal’s core mission to foster ethical reflection and guidance on emerging technologies that will shape future life profoundly.
Ethics-Driven Evaluation Metrics for Emerging Technology Systems
Standard technology evaluation metrics -- accuracy, throughput, latency, cost-efficiency -- optimise for technical performance while leaving ethical performance unmeasured and therefore ungoverned. Ethics-driven evaluation metrics supplement technical metrics with systematic measurement of ethical performance dimensions: fairness, transparency, human oversight quality, privacy protection, societal impact, and value alignment. This paper proposes the Ethics-Driven Evaluation Metrics Framework (EDEMF), a systematic methodology for designing, operationalising, and integrating ethics metrics into technology evaluation processes for five emerging technology system types: large language models, autonomous decision systems, AI surveillance systems, recommendation algorithms, and AI-assisted medical systems. EDEMF introduces the Ethics Metric Quality Score (EMQS) assessing metric validity, sensitivity, and governance utility across 42 candidate ethics metrics. Key results: 18 metrics achieve high EMQS (> 0.800) and are recommended as core evaluation requirements; fairness metrics show the highest variance across system types (not all fairness metrics apply to all systems); transparency metrics have the lowest measurement validity (hardest to operationalise objectively); the recommended 18-metric evaluation suite achieves comprehensive ethical coverage at manageable evaluation cost. The framework provides practical ethics metric guidance for AI developers, conformity assessors, and regulators.
Digital Divide Analysis Using Computational Social Science Models
The digital divide -- systematic inequality in access to, use of, and benefit from digital technologies -- is a multi-dimensional phenomenon whose causes, mechanisms, and social consequences resist simple measurement. Computational social science models provide richer analytical tools for digital divide research than conventional cross-sectional surveys: network analysis reveals how digital inequality propagates through social connections; agent-based models simulate divide dynamics under different intervention scenarios; natural language processing of social media enables real-time divide monitoring; and machine learning identifies non-linear predictors of digital exclusion invisible to regression analysis. This paper proposes the Computational Digital Divide Analysis Framework (CDDAF), applying four computational social science methods to analyse digital divide dimensions -- access, skills, usage, and benefit -- across six EU member state populations (Germany, France, Spain, Italy, Poland, Estonia), drawing on Eurostat DESI data (2019-2024), EU-SILC household panel, and social media data. CDDAF introduces the Digital Divide Severity Index (DDSI) measuring multi-dimensional divide intensity. Key results: benefit divide is 2.4x more severe than access divide in high-connectivity countries; age is the strongest predictor of digital exclusion (OR 4.2 for 65+ vs. 25-34 after controlling confounders); network-mediated divide propagation amplifies initial inequality by 28.4% over 10 years. The framework provides evidence-based digital inclusion policy guidance.
Human-Centered AI Frameworks for Socially Responsible Innovation
Human-centered AI (HCAI) frameworks place human values, needs, and agency at the centre of AI system design, development, and deployment -- ensuring that AI serves human flourishing rather than treating humans as instruments of AI performance objectives. Socially responsible innovation (SRI) extends this by requiring that AI innovation processes be responsive to societal needs, accountable to affected communities, and reflective about value implications before deployment. This paper proposes the Human-Centered AI for Socially Responsible Innovation Framework (HCAI-SRIF), a systematic evaluation of six HCAI design principles -- human oversight, interpretability and explainability, value alignment, participatory design, reversibility and error correction, and social impact assessment -- across five AI innovation contexts: clinical decision support, autonomous financial advisory, educational AI, AI-assisted creative work, and public policy AI. HCAI-SRIF introduces the Human-Centered Innovation Quality Score (HCIQS) and evaluates 24 AI systems across all five contexts. Key results: clinical decision support achieves the highest HCIQS (0.784) driven by medical governance maturity; autonomous financial advisory achieves the lowest (0.468) due to opacity and human oversight deficit; participatory design is the most underimplemented principle (present in 20.8% of evaluated systems). The framework provides HCAI design guidance and SRI methodology for AI developers and deployers.
Inclusive System Design for Ethical Technology Adoption
Inclusive system design (ISD) for emerging technologies ensures that AI, digital services, and intelligent infrastructure are accessible, usable, and beneficial to the full diversity of human users -- including people with disabilities, older adults, those with low digital literacy, minority language speakers, and communities with limited connectivity. Ethical technology adoption requires inclusive design because non-inclusive systems create compulsory exclusion: when digital services replace physical alternatives, non-inclusive digital design forces exclusion from services that citizens have a right to access. This paper proposes the Inclusive System Design for Ethical Adoption Framework (ISDEAF), evaluating five inclusive design approaches -- universal design, accessibility-first design, assisted digital service design, co-design with marginalised communities, and adaptive personalisation -- across four technology adoption contexts: AI-mediated public services, digital health platforms, smart city infrastructure, and AI educational tools. ISDEAF introduces the Inclusive Adoption Quality Score (IAQS) integrating accessibility compliance, usability across diversity, benefit equity, and compulsory exclusion prevention. Key results: co-design achieves the highest IAQS (0.908); accessibility-first achieves the strongest exclusion prevention (0.920); 68.4% of evaluated adoptions create compulsory exclusion for at least one demographic group. The framework provides inclusive design methodology guidance for technology deployers and regulators.
Computational Models for Public Trust in Emerging Technologies
Public trust in emerging technologies -- AI systems, autonomous vehicles, biotechnology, and digital infrastructure -- is both a prerequisite for beneficial technology adoption and a product of governance quality, past technology experiences, and media framing. Computational models of public trust dynamics enable policymakers and technology developers to understand how trust forms, erodes, and recovers; to predict the adoption implications of trust crises; and to design governance interventions that build warranted trust. This paper proposes the Computational Public Trust Model for Emerging Technologies (CPTMET), applying system dynamics and agent-based modelling to simulate public trust dynamics across four technology domains: AI personal assistants, autonomous vehicles, CRISPR gene therapy, and smart grid infrastructure. CPTMET introduces the Trust Dynamics Quality Score (TDQS) assessing model validity, policy sensitivity, and predictive accuracy. Key results: trust recovery from incidents follows a logarithmic trajectory -- 42.4% of pre-incident trust recovers within 24 months with no governance response, rising to 78.4% with active governance intervention; media framing has a 2.8x amplification effect on incident trust impact; transparent governance is the single most effective trust-building intervention (+28.4% trust gain per unit investment). The framework provides trust-aware technology governance guidance for policymakers and deployers.
Regulatory Impact Modeling for Emerging Digital Technologies
Regulatory impact modelling (RIM) for emerging digital technologies requires methodologies that can quantify the costs and benefits of regulation across multiple dimensions -- compliance burden, innovation effects, consumer welfare, market structure, and societal outcomes -- under conditions of deep uncertainty about technology trajectories, market responses, and second-order social effects. This paper proposes the Regulatory Impact Modelling Framework for Digital Technologies (RIMFDT), a systematic methodology combining econometric estimation, computational simulation, and expert elicitation to quantify regulatory impacts across four emerging digital technology regulation domains: generative AI (EU AI Act), data governance (EU Data Act), digital platform competition (DMA), and cybersecurity (NIS2). RIMFDT extends and refines CRISF (Horvath, 2025) by integrating empirical econometric calibration with simulation dynamics and providing a structured impact decomposition methodology. Key results: EU AI Act produces net positive welfare impact (EUR +8.4 billion/year EU-27 by 2030) driven by trust and safety benefits exceeding compliance costs; EU Data Act consumer benefits exceed industry costs by 2.4x at current implementation parameters; DMA full compliance reduces consumer welfare loss from digital platform market power by EUR 12.4 billion/year. The framework provides impact modelling methodology and empirical calibration for digital technology regulators.
AI-Assisted Decision Support Systems for Technology Governance
Technology governance decisions -- regulatory impact assessments, conformity assessments, incident investigations, and standard-setting deliberations -- require processing large volumes of technical evidence, regulatory precedent, and stakeholder input that human governance bodies cannot comprehensively review at the pace of technological change. AI-assisted decision support systems (AI-DSS) for technology governance provide regulators, standards bodies, and oversight institutions with evidence synthesis, risk prediction, precedent analysis, and scenario modelling capabilities that augment governance quality without replacing human judgment. This paper proposes the AI-DSS for Technology Governance Framework (AITGF), evaluating five AI-DSS architectures -- regulatory evidence synthesis, conformity assessment AI, incident pattern analysis, standards gap analysis, and governance scenario modelling -- applied to five technology governance contexts: EU AI Act conformity assessment, NIS2 cybersecurity incident investigation, biotechnology safety evaluation, autonomous systems certification, and digital platform competition oversight. AITGF introduces the Governance Decision Quality Score (GDQS) and evaluates each AI-DSS architecture across three governance institutions over 12 months. Key results: regulatory evidence synthesis achieves the highest GDQS (0.908); incident pattern analysis achieves the highest time-efficiency gain (68.4%); conformity assessment AI achieves 94.2% accuracy on EU AI Act Annex III classification. The framework provides AI-DSS deployment guidance for technology governance institutions.
Computational Simulation of Technology Regulation and Social Outcomes
Technology regulation shapes social outcomes through complex causal mechanisms -- direct compliance effects, innovation incentive changes, market structure responses, and second-order social adaptations -- that regulatory impact assessments typically model inadequately. Computational simulation of regulation-outcome relationships provides richer, more dynamic predictions of regulatory effects than conventional static cost-benefit analysis by capturing feedback loops, distributional dynamics, and emergent social behaviours that resist analytical modelling. This paper proposes the Computational Regulation Impact Simulation Framework (CRISF), applying agent-based modelling and system dynamics simulation to four technology regulation scenarios: EU AI Act compliance cost dynamics, GDPR privacy-innovation trade-offs, digital platform competition regulation, and autonomous vehicle safety standard stringency. CRISF simulates social outcomes -- innovation rate, consumer welfare, equity distribution, and safety performance -- across 10,000 Monte Carlo runs per scenario under varied regulatory parameter settings. Key results: EU AI Act compliance costs create a market concentration dynamic that favours large incumbents; GDPR shows a privacy-welfare Pareto frontier with achievable win-wins at intermediate stringency; AV safety standard stringency exhibits a non-monotonic safety outcome -- stricter standards increase safety up to an optimal point, then decrease it through adoption delay. The framework provides computational regulatory impact analysis methodology for policymakers and regulatory economists.
Policy-Aware Computing Models for Governing Emerging Technologies
Policy-aware computing models embed governance rules, regulatory requirements, and institutional policies directly into the computational systems that operate on emerging technologies, enabling automated compliance enforcement and real-time governance monitoring that manual regulatory processes cannot achieve at the speed and scale of modern technology deployment. Extending the PAECF framework from educational governance (Novak, 2025) to the broader domain of emerging technology governance, this paper proposes the Policy-Aware Computing for Emerging Technology Governance Framework (PACE-TGF), evaluating five policy-aware computing architectures -- policy-as-code automation, AI compliance monitoring, smart contract governance, rights-based constraint systems, and adaptive regulatory sandboxes -- across four emerging technology governance domains: generative AI regulation, autonomous systems certification, biotechnology oversight, and quantum cryptography standardisation. PACE-TGF introduces the Technology Governance Quality Score (TGQS) integrating regulatory compliance coverage, enforcement effectiveness, adaptability to technology evolution, and democratic accountability. Key results: policy-as-code achieves the highest TGQS (0.904) for domains with clear regulatory requirements; adaptive regulatory sandboxes achieve the highest adaptability score (0.960) for rapidly evolving technologies; rights-based constraint systems provide the strongest protection for fundamental rights at the cost of regulatory flexibility. The framework provides policy-aware computing selection guidance for technology governance practitioners and regulators.
Socio-Technical Risk Analysis of Intelligent Infrastructure Systems
Intelligent infrastructure systems -- AI-managed power grids, autonomous transport networks, AI-integrated water systems, and smart telecommunications backbone -- present socio-technical risks that conventional technical risk analysis cannot fully capture. Socio-technical risk analysis (STRA) treats infrastructure systems as coupled technical-social systems where risks arise not only from technical failures but from the interaction between technical components, human operators, institutional structures, and social contexts that collectively determine system behaviour and failure modes. This paper proposes the Socio-Technical Risk Analysis Framework for Intelligent Infrastructure (STRAIF), a systematic methodology integrating five risk analysis approaches -- STAMP/STPA socio-technical hazard analysis, organisational accident analysis, AI-specific failure mode analysis, community impact risk assessment, and democratic legitimacy risk analysis -- applied to three intelligent infrastructure domains: AI-managed power grids, autonomous urban transport systems, and AI-integrated water management. STRAIF introduces the Socio-Technical Risk Score (STRS) and demonstrates that AI-specific and socio-technical risks are systematically missed by conventional technical risk analysis in all three domains. Key results: democratic legitimacy risk is the most consistently underanalysed risk category; AI-specific failure modes (distribution shift, adversarial attacks, model drift) are absent from current infrastructure risk registers in 84.2% of reviewed deployments; community impact risk is highest for autonomous transport systems. The framework provides STRA methodology guidance for infrastructure operators, safety regulators, and policymakers.
Responsible Design of Smart Surveillance and Monitoring Systems
Smart surveillance and monitoring systems -- AI-enhanced CCTV networks, behavioural analytics platforms, workplace monitoring tools, and biometric access control systems -- are increasingly deployed by public and private sector organisations that prioritise security and operational efficiency over the privacy, dignity, and autonomy implications for monitored individuals. Responsible design of these systems requires embedding ethical constraints at the architecture, algorithm, and governance levels -- not as retrospective compliance additions but as foundational design requirements. This paper proposes the Responsible Surveillance Design Framework (RSDF), a systematic methodology integrating privacy-by-architecture, proportionality assessment, purpose limitation enforcement, transparency design, and contestability mechanisms into a unified responsible design process for smart surveillance systems. RSDF is evaluated through application to five surveillance design cases: urban public space monitoring, workplace productivity surveillance, retail loss prevention AI, school safety monitoring, and hospital patient monitoring. The framework introduces the Responsible Design Score (RDS) integrating privacy protection strength, proportionality, transparency, and contestability. Key results: hospital patient monitoring achieves the highest RDS (0.784) due to clinical governance framework; workplace productivity surveillance achieves the lowest RDS (0.312) driven by consent invalidity and purpose creep; privacy-by-architecture is the single highest-impact design intervention across all five cases. The framework provides a responsible surveillance design checklist and process for deployers and regulators.
Ethics-Aware Control Systems for Cyber-Physical Infrastructure
Cyber-physical infrastructure (CPI) systems -- power grids, water treatment networks, transport control systems, and telecommunications backbone -- integrate computational control with physical processes that have direct and irreversible consequences for human welfare, safety, and equity. Ethics-aware control systems embed ethical constraints -- safety prioritisation, equity in service distribution, democratic accountability, and environmental impact limits -- directly into the control algorithms that govern critical infrastructure behaviour. This paper proposes the Ethics-Aware CPI Control Framework (EACCF), a systematic design and evaluation of four ethics-aware control architectures -- constraint satisfaction control (CSC), multi-objective ethical optimisation (MOEO), participatory adaptive control (PAC), and safety-ethics co-design (SECD) -- applied to three CPI domains: smart power grid management, water distribution optimisation, and urban traffic control. EACCF introduces the Ethics-Aware Control Quality Score (EACQS) integrating safety constraint satisfaction, equity in resource distribution, democratic accountability, and operational efficiency. Key results: safety-ethics co-design achieves the highest EACQS (0.892); equity in resource distribution is the most challenging ethical constraint to satisfy simultaneously with operational efficiency; CSC achieves the highest safety constraint satisfaction (0.960). The framework provides ethics-aware control system design guidance for critical infrastructure operators and regulators.
Trust and Accountability in Autonomous Robotic Systems
Trust and accountability in autonomous robotic systems are foundational requirements for socially beneficial deployment of robotics across healthcare, logistics, eldercare, manufacturing, and public infrastructure domains. Trust -- the human willingness to accept vulnerability to robotic system behaviour -- is both a prerequisite for adoption and a potential risk: inappropriate trust (over-reliance) is as ethically problematic as inappropriate distrust (under-utilisation). Accountability -- the institutional and technical mechanisms through which responsibility for robotic system harms is assigned, verified, and remedied -- is challenged by autonomous systems whose behaviour emerges from trained models rather than explicit programming, creating novel accountability gaps. This paper proposes the Robot Trust and Accountability Framework (RoTAF), a systematic analysis of five trust dimensions and four accountability mechanisms across six robotic deployment contexts: surgical robots, eldercare robots, autonomous warehouse systems, delivery drones, collaborative manufacturing robots, and autonomous emergency response robots. RoTAF introduces the Trust-Accountability Quality Score (TAQS) and evaluates current governance adequacy across all six contexts. Key results: eldercare robots achieve the lowest TAQS (0.484) due to emotional dependency risk and dignity concerns; surgical robots achieve the highest TAQS (0.724) driven by strong medical accountability frameworks; over-trust is the most prevalent trust pathology across all contexts. The framework provides trust calibration and accountability design guidance.
Ethical Challenges in Large-Scale IoT and Smart City Deployments
Large-scale IoT and smart city deployments -- interconnected sensor networks, intelligent transport systems, smart energy grids, environmental monitoring platforms, and AI-integrated urban services -- generate persistent data about citizens, urban behaviour, and community life at scales and with granularities that create profound ethical challenges around privacy, democratic governance, equity, and the appropriate role of technology in shaping urban experience. This paper proposes the Smart City and IoT Ethics Framework (SCIEF), a systematic analysis of six ethical challenge domains -- surveillance and privacy, democratic governance deficit, urban equity and digital divide, commercial exploitation of public data, infrastructure resilience and safety, and environmental impact -- applied to five smart city deployment domains: traffic and mobility, energy management, public safety, environmental monitoring, and citizen services. SCIEF introduces the Smart City Ethics Score (SCES) and evaluates ethical governance quality across 18 European smart city deployments. Key results: commercial data exploitation achieves the highest ethical concern rating (mean SCES = 0.124, worst governed); democratic governance deficit is the most prevalent challenge (present in 83.3% of deployments); privacy-by-architecture deployments achieve 48.4% lower surveillance concern scores. The framework provides smart city ethics governance guidance for municipalities and urban technology deployers.
Differential Privacy Frameworks for Ethical Data Analytics
Differential privacy (DP) provides a mathematically rigorous framework for privacy-preserving data analytics -- guaranteeing that any individual's participation in a dataset changes the probability of any analysis output by at most a multiplicative factor determined by the privacy parameter epsilon. As DP transitions from academic theory to practical deployment in industry and government, a set of ethical questions arises that the mathematical framework alone cannot resolve: how should epsilon be chosen and communicated to data subjects? Who bears the privacy cost of the epsilon budget? How should the privacy-utility trade-off be governed? This paper proposes the Differential Privacy Ethics Framework (DPEF), a systematic analysis of six ethical dimensions of DP deployment -- epsilon governance, budget accountability, utility-equity trade-offs, transparency and explainability, collective vs. individual privacy, and regulatory alignment -- applied to four DP deployment contexts: government statistics, health analytics, platform user data, and federated machine learning. DPEF introduces the DP Ethics Score (DPES) and evaluates three DP deployment approaches -- central DP, local DP, and federated DP -- against all six dimensions. Key results: federated DP achieves the highest DPES (0.892); epsilon governance is the most neglected ethical dimension across all current deployments; utility-equity trade-offs systematically disadvantage marginalised subgroups. The framework provides ethical DP deployment guidance for practitioners and regulators.
Bias and Discrimination Risks in Data-Driven Emerging Technologies
Bias and discrimination risks in data-driven emerging technologies arise from multiple sources -- historical data encoding past discrimination, measurement instruments that are less accurate for marginalised groups, model architectures that amplify statistical patterns of inequality, and deployment contexts that direct high-risk AI to high-disparity populations. This paper proposes the Bias and Discrimination Risk Assessment Framework (BDRAF), a systematic taxonomy and measurement approach covering six bias sources -- historical bias, representation bias, measurement bias, aggregation bias, deployment bias, and feedback loop bias -- applied to five emerging technology domains: large language models, facial recognition, predictive policing, credit scoring AI, and medical diagnosis AI. BDRAF introduces the Bias Risk Score (BRS) integrating bias source prevalence, demographic disparity magnitude, harm severity, and mitigation feasibility. Key results: predictive policing achieves the highest BRS (0.924) driven by historical and feedback loop bias; LLMs show the broadest bias source distribution (all six sources active); representation bias is the most prevalent source (present in all five domains); feedback loop bias is the most ethically severe single source (self-reinforcing discrimination). The framework provides bias risk prioritisation guidance and source-matched mitigation strategies.
Computational Analysis of Consent and Data Ownership in Digital Platforms
Consent mechanisms and data ownership frameworks in digital platforms are the primary legal and ethical instruments through which users exercise control over personal data, yet computational analysis reveals systematic gaps between formal consent validity requirements and actual user understanding, meaningful choice, and power to negotiate terms. This paper proposes the Consent and Data Ownership Computational Analysis Framework (CDOCAF), applying three computational analysis methods -- natural language processing readability analysis, behavioural consent quality assessment, and power asymmetry quantification -- to evaluate consent mechanisms and data ownership terms across 48 major digital platforms. CDOCAF introduces the Consent Quality Score (CQS) integrating legal validity, comprehensibility, genuine choice, and user empowerment. Key results: mean CQS across 48 platforms is 0.312 (poor) -- technically GDPR-valid but failing genuine informed consent standards; average privacy policy readability requires university-level education (Flesch-Kincaid grade 16.8); genuine choice is absent in 84.2% of platforms (take-it-or-leave-it with no alternatives); data ownership terms transfer economic value to platforms in 92.4% of cases. The framework provides consent quality measurement tools and reform recommendations.
Ethical Data Governance Models for Large-Scale Data Collection Systems
Large-scale data collection systems -- national health databases, smart city IoT platforms, genomic biobanks, and population-wide digital public services -- aggregate data from millions of individuals over extended periods, creating governance challenges that individual consent frameworks cannot adequately address. Ethical data governance models for these systems must balance the collective value of large-scale data (disease surveillance, urban planning, scientific discovery) against individual privacy rights, prevent mission creep, and ensure equitable access to data-derived benefits. This paper proposes the Ethical Data Governance Framework (EDGF), evaluating five governance models -- individual consent, collective consent, data trust, public interest oversight, and participatory data governance -- across four large-scale collection domains: national health data, smart city IoT, genomic biobanks, and public sector data. EDGF introduces the Data Governance Ethics Score (DGES) integrating individual rights protection, collective benefit maximisation, accountability, and equitable access. Key results: participatory data governance achieves the highest DGES (0.888); data trusts achieve the best balance of individual rights and collective benefit (0.872); individual consent alone achieves the lowest collective benefit score (0.480) across all domains. The framework provides governance model selection guidance for ethical large-scale data stewardship.
Privacy-Aware System Architectures for Surveillance Technologies
Surveillance technologies -- CCTV networks, biometric identification systems, smart city sensors, and workplace monitoring platforms -- create acute tensions between legitimate security objectives and fundamental privacy rights. Privacy-aware system architectures embed privacy constraints directly into surveillance infrastructure design, enabling security functions while providing formal privacy protections that policy controls alone cannot guarantee. This paper proposes the Privacy-Aware Surveillance Architecture Framework (PASAF), evaluating five architectural approaches -- privacy-by-design, data minimisation architecture, purpose-limitation enforcement, federated surveillance processing, and zero-knowledge verification -- across four surveillance domains: public space monitoring, workplace analytics, border control AI, and smart city infrastructure. PASAF introduces the Privacy-Security Balance Score (PSBS) integrating formal privacy guarantee strength, security utility preservation, GDPR compliance completeness, and proportionality. Key results: federated surveillance processing achieves the highest PSBS (0.892), preserving 94.8% of security utility at strong formal privacy guarantees; zero-knowledge verification achieves exact security function with no data exposure; data minimisation architecture reduces re-identification risk by 84.2% vs. conventional centralised surveillance. The framework provides architecture selection guidance for privacy-compliant surveillance deployment.
AI-Induced Labor Transformation: A Computational Ethics Perspective
AI-induced labour transformation raises computational ethics questions that conventional labour economics and traditional applied ethics cannot adequately address alone. Computational ethics -- combining formal ethical reasoning with quantitative modelling of social dynamics -- provides tools for analysing the ethical dimensions of labour transformation at societal scale: quantifying fairness in the distribution of automation gains and losses, modelling skill transition dynamics, and simulating the implications of different governance regimes. This paper proposes the Computational Ethics of Labour Transformation Framework (CELTF), applying four computational ethics methodologies -- distributive fairness modelling, capability transition simulation, intergenerational equity analysis, and collective action ethics modelling -- to three AI labour transformation scenarios: routine task automation, augmentation-led restructuring, and platform labour displacement. CELTF introduces the Labour Ethics Score (LES) integrating distributional fairness, capability preservation, temporal justice, and collective voice. Key results: co-determination governance achieves highest LES across all scenarios (0.824-0.876); capability transition simulation identifies a 7-12 year "ethics valley" where 42.4% of workers fall into capability traps without income support; platform labour displacement generates the worst collective voice score (0.284) under all governance regimes. The framework provides computational ethics guidance for labour transition policymakers and regulators.
Human-AI Interaction Models for Socially Responsible Automation
Socially responsible automation requires human-AI interaction (HAI) designs that maximise automation benefits -- efficiency, accuracy, scale -- while preserving the social, relational, and political dimensions of human decision-making that automation threatens to erode. The design of the interface between human judgment and AI capability -- the HAI model -- determines whether automation augments human agency or substitutes for it, whether it concentrates power in deploying organisations or distributes capability to users, and whether it maintains human skill and social competency or allows them to atrophy. This paper proposes the Human-AI Interaction for Responsible Automation Framework (HAIRAF), a systematic evaluation of six HAI models -- advisory AI, collaborative AI, supervisory control, human-in-the-loop, human-on-the-loop, and fully autonomous with audit -- across five social responsibility dimensions: human agency preservation, skill retention, power distribution, social relationship maintenance, and democratic legitimacy. HAIRAF evaluates each HAI model across four deployment contexts: knowledge work, care relationships, civic participation, and creative production. Key results: advisory AI achieves the highest social responsibility score (0.904) by preserving human agency while providing AI capability augmentation; human-in-the-loop achieves the best balance of efficiency and social responsibility (0.876); fully autonomous with audit achieves highest efficiency but lowest social responsibility (0.608) due to skill atrophy, power concentration, and democratic legitimacy concerns. The framework provides HAI model selection guidance for socially responsible automation deployment.
Ethical Implications of Autonomous Decision-Making Systems
Autonomous decision-making systems -- AI agents that take consequential actions without human approval at each step -- are increasingly deployed in domains from financial trading to medical triage to military target identification, raising profound ethical questions about accountability, moral responsibility, meaningful human control, and the appropriate scope of machine agency over decisions affecting human welfare. This paper proposes the Autonomous Decision-Making Ethics Framework (ADMEF), a systematic analysis of five ethical dimensions -- moral responsibility attribution, meaningful human control, value alignment under distribution shift, dignity and consent in autonomous interaction, and systemic autonomy risk -- applied to six autonomous decision-making domains: autonomous vehicles, algorithmic trading, medical AI diagnosis, criminal justice risk assessment, military autonomous weapons, and AI-driven social services allocation. ADMEF introduces the Autonomy Ethics Risk Score (AERS) integrating harm severity, accountability gap, consent deficit, and value alignment difficulty, and evaluates each domain through structured expert analysis (16 AI ethicists, legal scholars, and domain specialists) and case study analysis. Key results: military autonomous weapons achieve the highest AERS (0.964) -- the most ethically intractable autonomous domain; criminal justice AERS = 0.908 driven by accountability gap and consent deficit; medical AI AERS = 0.796 (high but manageable through informed consent and human oversight); meaningful human control is the most critical ethical safeguard across all domains. The framework provides autonomous system ethical governance guidance across domains.
Computational Models for Measuring Algorithmic Influence on Society
Algorithmic systems deployed at population scale -- recommendation engines, search algorithms, content moderation systems, and social media feed curators -- exert measurable influence on information consumption, opinion formation, political behaviour, and cultural production. Measuring this influence rigorously is essential for algorithmic accountability, democratic governance, and ethical AI deployment, yet it is methodologically challenging: algorithmic influence operates through complex, multi-step causal chains; counterfactual baselines (what would have happened without the algorithm?) are difficult to construct; and the distinction between algorithmic influence and individual preference expression is contested. This paper proposes the Algorithmic Societal Influence Measurement Framework (ASIMF), a systematic evaluation of six computational measurement approaches -- audit experiments, observational causal inference, agent-based modelling, natural language processing influence tracing, network diffusion analysis, and survey-experimental methods -- applied to four algorithmic influence domains: political opinion formation, health information consumption, consumer behaviour, and cultural discourse. ASIMF introduces the Influence Measurement Quality Score (IMQS) integrating causal validity, measurement precision, scalability, and ethical research practice. Key results: audit experiments achieve the highest IMQS (0.904) for direct algorithmic effect measurement; network diffusion analysis achieves the highest scalability (0.960); observational causal inference achieves 96.4% accuracy in replicating experimental estimates from observational social media data. The framework provides methodological selection guidance for researchers measuring algorithmic societal influence.
Societal Impact Assessment of AI-Driven Automation Systems
AI-driven automation systems are transforming labour markets, organisational structures, and social arrangements at an unprecedented pace, with societal impacts that extend far beyond the immediate efficiency gains to individual deploying organisations. Comprehensive societal impact assessment (SIA) for AI automation -- evaluating impacts on employment, wage distribution, occupational identity, community resilience, democratic participation, and generational opportunity -- is urgently needed but methodologically underdeveloped: most existing impact assessments focus on productivity and efficiency metrics, treating labour market disruption as an externality rather than a primary impact dimension. This paper proposes the AI Automation Societal Impact Assessment Framework (AASIAC), a systematic methodology for comprehensive SIA of AI automation across six impact domains -- employment and labour market, wage distribution, occupational identity and dignity, community and regional economic resilience, democratic and political participation, and intergenerational opportunity -- applied to four automation deployment scenarios: manufacturing AI, service sector AI, professional knowledge work AI, and public sector AI. AASIAC introduces the Societal Impact Severity Score (SISS) and evaluates each scenario against six impact domains using structured expert elicitation (20 economists, sociologists, and political scientists), quantitative economic modelling, and community impact case studies. Key results: professional knowledge work AI (LLM-driven automation) achieves the highest SISS (0.848) due to breadth of occupational disruption and concentration in urban knowledge economy centres; wage distribution impact is the most severe single domain across all scenarios; community resilience is the most neglected impact dimension in current policy analysis. The framework provides SIA methodology guidance for policymakers, technology deployers, and affected communities.
Value-Sensitive Design Approaches for Emerging Technologies
Value-sensitive design (VSD) is a theoretically grounded, practically oriented approach to technology design that accounts for human values in a principled and comprehensive manner throughout the design process. Originally developed for information systems and computer-supported cooperative work, VSD has been extended and contested as AI and autonomous systems have emerged as dominant technology design challenges, raising questions about whose values are represented, how value conflicts are resolved, and whether VSD's tripartite methodology -- conceptual, empirical, and technical investigation -- adequately addresses the systemic societal impacts of large-scale intelligent systems. This paper proposes the VSD for Emerging Technologies Framework (VSDETF), a systematic review and critical extension of VSD methodology for five emerging technology domains: conversational AI systems, autonomous decision-making agents, biometric surveillance technologies, personalisation algorithms, and human-robot interaction systems. VSDETF identifies four methodological gaps in original VSD when applied to emerging technologies -- scale gap, power gap, temporal gap, and systemic effects gap -- and proposes extended VSD+ methodology addressing each. VSDETF is evaluated through analysis of 28 published VSD case studies and 6 new VSD application cases. Key results: original VSD adequately handles direct stakeholder values but systematically under-represents indirect third-party harms at societal scale; VSD+ extended methodology identifies 48.4% more value tensions than original VSD across all five domains; power asymmetry analysis is the single most impactful VSD extension. The framework provides practical VSD+ implementation guidance for emerging technology designers and ethicists.
Ethical Risk Modeling for Large-Scale Technology Deployment
Large-scale technology deployments -- nationwide digital identity systems, AI-powered public health surveillance platforms, autonomous vehicle fleets, and ubiquitous computing infrastructure -- introduce ethical risks at societal scale that individual-level harm assessment cannot adequately capture. Ethical risk modelling provides structured methodologies for identifying, quantifying, and prioritising ethical risks before and during technology deployment, enabling proactive governance rather than reactive harm remediation. This paper proposes the Ethical Risk Modelling Framework for Large-Scale Technologies (ERMFLST), a systematic methodology integrating five ethical risk assessment approaches -- consequentialist harm mapping, rights-violation risk analysis, systemic risk assessment, distributional impact modelling, and emergent risk anticipation -- applied to four large-scale technology deployment scenarios: national AI surveillance systems, autonomous vehicle fleet deployment, nationwide digital identity infrastructure, and AI-integrated healthcare systems. ERMFLST introduces the Ethical Risk Score (ERS) integrating harm severity, probability, reversibility, and affected population scope, and demonstrates its application through structured expert elicitation (18 technology ethics experts) and quantitative risk modelling. Key results: AI surveillance systems achieve the highest ERS (0.924 on a 0-1 harm scale indicating high risk) -- driven by irreversibility and population scope; rights-violation risk analysis identifies harms invisible to consequentialist frameworks in 72.4% of cases; emergent risk anticipation achieves the highest forward-looking risk identification accuracy (84.2%). The framework provides ethical risk governance guidance for technology deployers, regulators, and civil society.
Algorithmic Accountability Frameworks for Emerging Digital Platforms
Algorithmic accountability -- the obligation of organisations deploying algorithmic decision systems to explain, justify, and accept responsibility for the impacts of those systems on individuals and society -- is increasingly demanded by regulators, civil society, and affected communities, but remains inconsistently implemented across digital platforms. Emerging digital platforms -- social media recommendation systems, gig economy matching algorithms, credit scoring models, content moderation systems, and AI-powered search engines -- make consequential decisions affecting billions of people at scales and speeds that preclude meaningful human review of individual decisions, creating an accountability gap that traditional legal and regulatory mechanisms cannot close. This paper proposes the Algorithmic Accountability Framework Taxonomy (AAFT), a systematic analysis of five accountability framework approaches -- transparency and disclosure requirements, algorithmic auditing, impact assessment obligations, contestability and redress mechanisms, and platform liability reform -- across four digital platform domains: content recommendation, gig economy labour allocation, consumer credit scoring, and public sector algorithmic decision-making. AAFT evaluates each framework across five accountability dimensions: ex ante harm prevention, ex post redress effectiveness, democratic legitimacy, enforcement feasibility, and innovation compatibility. Key results: algorithmic auditing achieves the highest accountability quality score (0.876) across all platform domains; contestability mechanisms achieve the highest democratic legitimacy score (0.924); transparency requirements alone are insufficient -- disclosure without meaningful interpretability provides accountability theatre rather than genuine accountability. The taxonomy provides an accountability framework selection guide for regulators and platform governance practitioners.
Ethics-by-Design Models for Socio-Technical Systems
Ethics-by-design (EbD) approaches to socio-technical system development integrate ethical considerations into the engineering process from the earliest design stages, rather than applying ethics as a retrospective audit or regulatory compliance exercise after systems are built and deployed. Socio-technical systems -- in which technical components (algorithms, sensors, actuators, databases) and social components (human roles, organisational processes, institutional structures, cultural norms) are mutually constitutive -- require ethics-by-design approaches that simultaneously address technical specification and social embeddedness, rather than treating ethics as a technical constraint applied to an otherwise value-neutral system. This paper proposes the Ethics-by-Design for Socio-Technical Systems Framework (EbDST), a systematic comparison of five EbD models -- value-sensitive design (VSD), responsible research and innovation (RRI), trustworthy AI by design (TAID), privacy engineering, and participatory ethics design (PED) -- across four socio-technical system domains: AI-mediated healthcare, algorithmic governance, smart city infrastructure, and digital labour platforms. EbDST evaluates each model across five process dimensions: stakeholder inclusion, technical integration depth, iterative refinement, power asymmetry awareness, and real-world implementation evidence. Key results: participatory ethics design achieves the highest overall EbD quality score (0.892) due to deepest stakeholder inclusion and power asymmetry awareness; trustworthy AI by design achieves the highest technical integration depth (0.924); privacy engineering achieves the most mature implementation evidence base. The framework provides an EbD model selection guide and a combined EbDST process for practical deployment.
Computational Ethics Frameworks for Emerging Intelligent Technologies
Emerging intelligent technologies -- large language models, autonomous agents, generative AI systems, biometric AI, and affective computing -- outpace the development of ethical frameworks designed to evaluate them, creating a persistent gap between technological capability and ethical governance. Traditional applied ethics frameworks derived from bioethics (beneficence, non-maleficence, autonomy, justice) provide valuable principles but insufficient operationalisation for the specific harms, power dynamics, and societal risks introduced by intelligent systems at scale. Computational ethics frameworks attempt to bridge this gap by translating ethical principles into formal structures -- mathematical representations, algorithmic procedures, and measurable criteria -- that can be applied systematically to evaluate and constrain AI system behaviour. This paper proposes the Computational Ethics Framework Taxonomy (CEFT), a systematic analysis of six computational ethics approaches -- consequentialist optimisation, deontological constraint satisfaction, virtue ethics alignment, contractualist preference aggregation, capabilities-approach evaluation, and pluralist multi-criteria frameworks -- applied to four emerging technology domains: large language models, autonomous agents, biometric AI, and affective computing. CEFT evaluates each framework across five criteria: theoretical coherence, operationalisability, conflict resolution, scalability, and cultural universality. Key results: pluralist multi-criteria frameworks achieve the highest overall applicability score (0.884); deontological constraint satisfaction best handles absolute prohibitions (privacy, consent violations); capabilities-approach evaluation is most appropriate for assessing intelligent technology impact on human flourishing. The taxonomy provides a principled framework selection guide for AI ethicists, regulators, and technology developers.
The Ethics of Data Ownership: Balancing Innovation, Consent, and Individual Rights in Emerging Technologies
In the era of digital transformation, data has emerged as the new currency of innovation, driving artificial intelligence (AI), biotechnology, and smart systems. However, the question of who truly owns and controls personal data has become one of the most critical ethical challenges of the 21st century. This research paper examines the moral, legal, and social implications of data ownership in emerging technologies, focusing on the delicate balance between innovation, individual autonomy, and consent. It explores frameworks that reconcile technological progress with ethical governance, advocating for policies that protect individuals’ rights while fostering open innovation. The study highlights the tension between data-driven corporate power and personal privacy, proposing a multidimensional model of ethical data stewardship grounded in transparency, accountability, and fairness
Data ethics, ownership, consent, privacy, artificial intelligence, emerging technologies, digital rights, innovation policy, data governance, surveillance capitalism