ALT Editorial policies increasingly require authors to disclose substantive uses of generative AI, but such requirements commonly focus on whether, where, and how a model was used. Even function-specific disclosure may locate a model in the research or writing process without showing how its outputs entered scholarly judgment, how they were evaluated, or whether contributors can reconstruct the relevant reasoning and defend resulting claims without treating those outputs as sufficient justification. Drawing on research ethics, AI disclosure policy, human-model interaction, and researcher formation, this article argues that disclosure documents model use, whereas responsible use requires claim-level authorial warrant: a contributor's capacity to understand, justify, and defend claims within their contribution. The article distinguishes cognitive delegation, in which model outputs shape substantive choices, interpretations, or justifications, from routine delegation, which does not ordinaril

ALT Large-area accessibility analysis remains difficult to reproduce because multimodal routing, heterogeneous opportunity data, and reusable spatial outputs are rarely integrated in a single transparent workflow. We present MAPPER (Multimodal Accessibility and Place Profiling Engine for Arbitrary Regions), an open-data pipeline for multimodal accessibility analytics. The workflow builds walk, bike, car, and public transport travel-time graphs from OpenStreetMap and GTFS data, harmonizes mapped POI- and proxy layers representing the land-use side of the accessibility relation, and computes continuous accessibility fields using network kernel density estimation. A low-overhead native compute core (contraction-hierarchy routing, C++/OpenMP accessibility accumulation, DAG-style orchestration eliminating redundant computation) is the enabler that makes many graphs, large extents, and a broad layer set jointly tractable on commodity hardware. Outputs are sampled to a uniform H3 grid for downstr

ALT Governments govern AI-generated online harms from commercial platforms (EU DSA, AI Act, UK Online Safety Act), yet the most severe class remains ungoverned: autonomous cyber weapons (ACWs) deployed by nation-states against civilian infrastructure. We address this gap with three contributions: a digital-harm taxonomy (six ACW modes × five harm categories); the Meaningful Human Control (MHC) Model, a governance-compliance function with a closed-form IHL threshold and a proved weight-sensitivity bound; and a platform accountability architecture with treaty language grounded in ARSIWA state-responsibility doctrine, not Rome Statute individual responsibility. Applied to eleven documented state-attributed operations and one hypothetical defensive AI system, every documented case falls in an accountability vacuum; the threshold is target-dependent, and against wholly civilian targets no system studied clears it, including the hypothetical. Three further incidents are correctly excluded on aut

ALT No domestic legislature has ever reviewed a state's decision to authorise an autonomous cyber weapon before it acted. Closing the international attribution gap is necessary but not sufficient for democratic accountability. We formalise the Tripartite Attribution Framework's three attribution levels and add a fourth, non-legal Democratic Accountability level (A_dem): a 72-hour legislative notification requirement. Scoring six democratic oversight bodies against five adoptability dimensions yields a counter-intuitive result: the statutes granting these bodies privileged access to classified material are often the same statutes withholding particular ongoing operations from them, so at most one could lawfully receive such a notification today, and the carve-out Level 4 needs is required in nearly every jurisdiction rather than only the least-equipped. Retrospective analysis of Stuxnet, NotPetya, and Volt Typhoon shows one pattern: legal attribution resolvable, domestic democratic review a

ALT Text-to-image (T2I) systems do not merely render prompts; they circulate defaults for "what X looks like," shaping recognition through pose, camera, costume, props, and setting. While technical audits quantify disparities, they rarely cultivate the literacy needed to argue how images assemble stereotypes and confer authority. This paper translates a three-stage, humanities-informed framework into a set of epistemic games that make this cultural work visible and debatable: Interpret (art-historical close looking with AI outputs), Interrogate (counter-intent prompting as artistic exploration), and Expose (creative misuse through paired prompts with a constant motif and a BiasSlot rule, in which only the bias cue varies). In a pilot with undergraduate fine-arts students, the games shifted discussion from impressionistic reactions to concise, warranted hypotheses grounded in visual evidence, supported by lightweight classroom instruments that enabled reproducibility and structured collabor

ALT Open-ended AI work has no single correct answer to check, so a judge, often another AI, decides each result. Research on these judges asks which AI to use and how to word the rubric. It rarely tests the procedure that the judge follows. Wargames have no correct answer either, and for two centuries the field has designed that procedure: named methods that set how much freedom the referee has, their known failures, and fixes. We map eleven wargaming practices onto AI evaluation failures. We test one, the adjudication method, on 378 player actions from nine games of an AI-refereed exercise. A player states its odds, the adjudicator turns them into a target number, and a 20-sided die decides pass or fail. With rolls fixed, the target came from the rulebook (Rigid), a judge AI adjusting it (Semi-Rigid), a judge AI setting it alone (Free) or a panel of four judge AIs. In evaluation terms these are a fixed scoring rule, a rubric the judge may adjust, holistic grading and a jury. The judge AIs

ALT AI models already inform clinical decisions and draft notes in Australian clinical software, usually without the practitioner being told which model is used. Existing medical benchmarks are written for other jurisdictions. We wrote 142 questions from Australian guidelines and schedules across five domains (PBS authority, MBS billing, Ahpra obligations, triage red flags, immunisation and screening), each with one verifiable answer scored by exact match. Each question ran blind and with the governing clause supplied, which separates failure to recall the current rule from failure to apply it. We tested 28 models between July and October 2026. Blind accuracy ranged from 51.5% to 92.7%, and the highest-scoring models could not be separated. Of 791 failed model-question pairs, 684 (86%) were wrong blind and right with the clause. Confident-error rates ranged from 6.6% to 47.8%. On five immunisation questions where the guideline gives no directive, 82% of answers stated one. On 20 Aboriginal

ALT Technology tools used for professional upskilling change on a timescale of weeks, and written guidance about them ages at the same pace. Large language models have made it inexpensive to draft a tutorial or a course outline, but most deployed generation systems still produce a piece of content once and never check it against what an audience actually does with it. This paper describes an agentic pipeline, organized as a typed workflow graph, that treats instructional content production as a manufacturing process with explicit checkpoints rather than a single generation call. Four node classes structure the graph: deterministic scripts, model steps bound to a fixed rubric, open-ended agent phases, and human approval gates, arranged along a forward production path and closed by a feedback cycle that rewrites generation rules from post level engagement telemetry. Production of content is split into two layers: a demand layer, built from video and community sources, that shapes topic selec

ALT The rapid expansions of artificial intelligence (AI), big data (BD), and information and communication technologies (ICT) are driving substantial growth in electricity, water, and material demands, with data centers projected to consume 6.7–12% of U.S. electricity by 2028 (up from 4.4% in 2023) and AI-related water demand estimated at 4–6 billion m³ annually by 2027. Existing “green AI” and “red AI” discussions have largely emphasized computational efficiency and carbon emissions, providing limited guidance for lifecycle trade-offs, service-versus-use impacts, and broader sustainability externalities beyond energy. To bridge this gap, this paper proposes generalized green AI (GGAI) and generalized red AI (GRAI) and extends the taxonomy to red BD (RBD) and red ICT (RICT) as well as red electronic and electrical engineering (REEE) & red digital technologies (RDT) under a unified Environmental-Economic-Social-Technical/Operational (E/Ec/S/T) framework. Based on representative footprint in

ALT As the use of AI, and consequently its harms, becomes more prevalent, the global discourse around AI has become increasingly risk-aware. With new risks emerging more frequently than ever, a key challenge is how to represent, document, and catalogue AI risks in an effective, interoperable, and queryable manner. Additionally, the European Union (EU) AI Act's requirements for "high-risk" AI systems to manage and document the risks creates the need for open and interoperable tools, methods, and standards that facilitate AI risk management in an ever-changing environment. In this paper, we present ontologies and taxonomies developed to assist with risk management and documentation, in line with the requirements of the EU the AI Act. We developed the AI Risk Ontology (AIRO) to specify AI use cases and their associated risks, complemented by the Vocabulary of AI Risks (VAIR), which populates AIRO with taxonomies of concepts. Key international standards on AI and risk, namely ISO/IEC 23894 on

ALT ClinGuard is a model-agnostic governance framework that treats clinical coordination as patient-scoped, policy-bound transactions rather than free text. An independent sentinel layer validates and governs agent-proposed actions, escalates when needed, and executes approved actions through limited-privilege connectors. Each transaction is linked to a tamper-evident audit record, while an outcomes monitor informs future autonomy decisions. Using MIMIC-IV-FHIR and skilled-nursing-facility placement, we distinguish retrospective demonstration from proposed prospective evaluation in this work.

ALT In the last decade and in particular since 2018, supranational institutions have published policies, guidelines, and most recently regulations to govern development and use of Artificial Intelligence (AI). This paper aims to investigate the themes and dimensions of “AI success” set forth by instruments of AI governance issued by supranational bodies. To this end, we use a mixed-methods approach, combining manual thematic analysis and automated topic modelling approaches, to analyse a curated corpus of 13 documents issued by supranational bodies, including the European Union bodies, OECD, G20, UNESCO, United Nations, and the Council of Europe between 2018-2025. Based on the analysis and through the lens of sociology of expectations, we propose a novel categorisation scheme for AI success themes emerging from supranational bodies' documents. Drawing on the findings, we advance a set of policy recommendations, setting forward the need to move beyond high-level principles toward more coord

ALT We propose “DLT Terms 100” aimed at improving literacy regarding blockchain (distributed ledger technology: DLT). The pace of DLT's social implementation has been slower than initially expected, partly due to a shortage of engineers and literate individuals. In particular, the discrepancy between business and technical terminology, and the confusion caused by the repurposing of existing financial terms, are barriers to understanding the concepts and technologies. Our proposed glossary is based on ISO 22739:2024, and categorizes and labels terms based on their frequency of appearance in representative domestic and international literature and their difficulty level.

ALT This paper argues that AI-native academia should build a Community Verification Layer: interoperable, policy-governed infrastructure that converts researchers' routine reading and checking into persistent, claim- and version-linked evidence, while routing scarce deeper verification toward uncertain, consequential, and neglected research. AI is lowering the cost of producing plausible research artifacts faster than the cost of accountable evaluation, yet much of the evaluation researchers already perform while reading, citing, implementing, or extending work is never made reusable. We propose a tiered ladder from comprehension notes to claim checks, audits, artifact execution, reproduction, replication, and downstream validation. The layer would elicit judgments before revealing social signals, preserve multidimensional disagreement, privately verify contributors while allowing flexible public disclosure, allocate attention under expertise and coverage constraints, and make verification

ALT This inaugural keynote lecture surveys the state of the art of the right to be forgotten and examines how it is being transformed by artificial intelligence. Drawing on cognitive science, philosophy and the social sciences, it treats forgetting as a constitutive function of memory, and recalls that remembering the past and imagining the future share the same mechanism. It then reviews the origins, the European and national case law, and the paradoxes of the right, and offers a periodisation of the specialised literature between 2009 and 2026. The core claim is that digital memory has moved through three regimes, archive, search engine and generative model, and that the right was designed for the first two. In the third, personal information is no longer stored as a locatable record but inferred and regenerated. Artificial intelligence prefigures the frameworks of individual and collective remembering. The right to be forgotten thus ceases to be merely an individual ex post remedy and b

ALT In March 2026, Bill C-12 amended the Immigration and Refugee Protection Act to make refugee claims ineligible for referral to the Refugee Protection Division where they are made more than one year after the claimant's first entry to Canada. Affected claimants are diverted to a Pre-Removal Risk Assessment (PRRA), which is typically decided by an immigration officer on a paper record, with no guaranteed oral hearing, no appeal on the merits, and no automatic stay of removal pending judicial review. The government defended the bar partly on the premise that genuine refugees claim promptly. Critics countered that late claims are concentrated among vulnerable groups, particularly people fleeing persecution based on gender, sexual orientation, gender identity, or gender expression (SOGIE). This article tests those competing premises. Using artificial intelligence tools, checked through human review, it examines nearly 29,000 published decisions of the Refugee Protection Division, the Refugee

ALT In response to vast and widespread online tracking, regulators and technologists alike have worked to develop laws and tools that allow people to express their privacy preferences. These measures seek to offer users greater control over the sale and sharing of personal information, putting agency in their hands to protect their privacy. However, the web is undergoing a seismic shift, away from strictly user-driven behavior towards agentic behavior, where AI agents take autonomous actions on behalf of users. In this study, we ask: What should privacy preference controls look like in the agentic web and agentic systems more generally? To answer this question, we develop a taxonomy of enforceable privacy preferences for agentic systems. Our taxonomy is informed both by laws and empirical studies of existing privacy tools as well as an audit of 30 leading AI assistants and agent platforms. To explore how our taxonomy can be implemented, we design and develop eight open-source prototypes th

ALT The infamous privacy paradox refers to the apparent inconsistency between people's stated concern for privacy and their readiness to disclose personal information. This phenomenon has sparked two largely disconnected literatures: one offering experimental evidence of inconsistent behavior, and another providing qualitative accounts and defending the importance of privacy. The Article presents an online field experiment that bridges those literatures and shows that the so-called paradox arises from a mischaracterization of the underlying behavior. The Article finds that it is structural uncertainty about risk that drives seemingly paradoxical privacy decisions. It does so by isolating discounting mechanisms and empirically testing whether observed privacy choices reflect temptation or rational responses to uncertainty. The results suggest that privacy behavior is not paradoxical but, rather, consistent with choices shaped by incomplete information. The Article then discusses the policy

ALT "Abliterated" ("uncensored") builds of open-weight language models remove the activation direction that mediates refusal, and are popular partly on the assumption that a model that no longer refuses is also free of political censorship. We test this on two matched pairs of 2-bit models: NVIDIA's Nemotron 3.5 Bolt 74B-A9B and Qwen3.8-Flash-Next, each against a community abliteration. Each model judged 22 statement pairs (on the Republic of China (Taiwan), 1989 Tiananmen, control countries) in Traditional Chinese, Simplified Chinese (a character-level conversion) and English, and answered 27 open questions per script. Script mattered far more than abliteration: the original Bolt scored +0.58 on Index S (-1: People's Republic of China (PRC) position; +1: ROC position) in Traditional Chinese, +0.57 in English and +0.05 in Simplified Chinese, and affirmed "Taiwan is part of the PRC" with probability 0.91 in Simplified but 0.06 in Traditional characters; abliteration changed its scores by at

ALT Large language models increasingly address users in the register of care: they remember personal details, adopt a first-person emotional voice, and sustain relationships across many sessions. This anthropomorphic mode of interaction generates risks that output-level metrics capture poorly, because the same empathic utterance may console one user, entrap a second, and consolidate a delusion in a third. Governance has responded in two ways. Uniform restriction refuses sensitive requests or suppresses empathic phrasing across all outputs; group-targeted protection profiles vulnerable populations and applies stricter or prohibitive rules to them. This article argues that both fail. Uniform restriction mislocates harm in the lexical form of responses rather than in the trajectory of a relationship; group-based protection depends on a list that can never be complete, treats a fluid property as a static attribute of persons, and converts protective attention into stigmatizing labels. Drawing