Transparency

Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumour progression in breast cancer patients while addressing four critical deployment pillars: transparency, scalability, security, and fairness. This study evaluates a federated learning fram

Defense and industry leaders outline how centralized accountability, industrial base funding, and gaming-engine floor training are modernizing naval shipyards. The post How technology and acquisition reform are propelling Navy submarine production appeared first on DefenseScoop .

Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable. We study a layered pipeline on the PaySim dataset that combines a gradient-boosted classifier, graph-derived structural features, an autoencoder-based anomaly signal, TreeSHAP explanations, and a bounded LLM investigation agent applied to cases the classifier scores uncertainly. Before any model comparison, we identify and remove a simulator-specific balance shortcut that would otherwise

States have made steady progress including students with disabilities in mainstream classrooms, an independent federal report finds, but lawmakers and advocates worry that headway will be lost as federal special education offices move from the Department of Education to Health and Human Services. Released this month, the Government Accountability Office report shows the number of […]

Evaluation frameworks for tool-augmented LLM agents focus overwhelmingly on capability metrics or explicit tool crashes, leaving silent infrastructure failures and HTTP 200 responses with empty, null, or malformed payloads largely unaudited. We introduce a lightweight black-box auditing framework that injects four silent failure profiles across 12 production-adjacent tool stubs and classifies agent responses into three mutually exclusive behavioral classes: Honest Surrender (HSR), Fabrication (F

Steps should be taken to strengthen grand juries by giving courts and defense counsel more tools to act as a much-needed check on prosecutors. The post Trust, But Verify: Three Grand Jury Reforms to Hold the Government Accountable appeared first on Just Security .

Web browsers now provide AI-generated news summaries for millions of users. Despite their popularity and influence, we lack a systematic understanding of how these systems transform news before people read it. Through a large-scale audit, we investigate the factual accuracy of browser-based AI summarizers and how they alter the political bias, negative affect, and journalistic writing quality of news. Drawing on 13,777 articles from 15 U.S. news outlets, we evaluate their 41,331 summaries genera

Machine unlearning is commonly evaluated by matching a retrained oracle on trained probes. In a controlled nonce-fact testbed with a matched retraining reference, we find this criterion can favor methods that retain held-out knowledge: candidates it rates adequate score held-out forget facts $-2.82$ nats below the never-learned level (cluster CI $[-3.16,-2.48]$). We recast unlearning as restoration to the matched reference and audit oracle-free screens and certificate-style criteria across 45 mo

Wearable photoplethysmography (PPG) provides continuous heart-rate measurements, but its accuracy degrades under motion. In the ring-platform benchmark, the best supervised baseline reaches 5.33 BPM mean absolute error (MAE) on the overall heart-rate task. In the motion-focused ring-only audit, a supervised LSTM baseline reaches $14.39 \pm 0.47$ BPM MAE on motion windows, and simple smoothing and ACC priors reduce this only to $13.00 \pm 0.41$ BPM. This thesis addresses motion-corrupted HR estim

arXiv:2607.17356v1 Announce Type: cross Abstract: Allegations that TikTok shadow bans political content shape what creators post, what advertisers fund, and how regulators act, yet they are hard to adjudicate because platforms do not disclose how content is ranked. We test the claim with a dense hourly panel of 556,946 follower-normalized views across 2,753 videos from 67 accounts curated into pro and anti sides of three contested topics (U.S. immigration enforcement, Trump coverage, and Israel/

arXiv:2607.02201v2 Announce Type: replace Abstract: The rapid deployment of AI systems across high-stakes domains has created urgent demand for standardized evaluation, yet the field remains fragmented across competing risk taxonomies that catalog risks without showing how an audit is executed. At least 74 AI risk taxonomies exist, and almost all stop at the catalog. The hard part of auditing is not naming a risk but operationalizing it: turning it into a test run against a real system, a measur

arXiv:2607.01730v2 Announce Type: replace-cross Abstract: Liquid democracy lets voters either vote directly or delegate their voting power to a trusted participant. Existing deployments make delegations publicly visible as they form, which invites popularity-driven herding, makes coercion verifiable, and leaves the election fragile when highly backed delegates abstain. We propose a liquid democracy mechanism that removes these vulnerabilities while keeping the tally fully auditable. Delegation c

Auditory attention decoding (AAD) identifies the speaker a listener attends to from neural responses like electroencephalography (EEG), making it a key algorithm in neuro-steered hearing aids. However, most neural AAD models are trained as independent short-window classifiers, despite auditory attention being a temporally persistent cognitive state and short-window EEG--audio evidence often being noisy and ambiguous. We propose an end-to-end Markov AAD framework based on conditional random field

CHEROKEE COUNTY, Ga. - Cherokee Sheriff's investigators arrested two agency members Monday after an internal database audit revealed multiple employees used automated license plate readers for non-law enforcement purposes. Georgia law viol ... (https://incidentdatabase.ai/cite/1595#7526)

IntroductionPulmonary fibrosis (PF) is a progressive interstitial lung disease that requires accurate and early detection to improve patient survival and treatment planning.MethodsThis study proposes an explainable deep learning framework for pulmonary fibrosis detection from chest X-ray images by integrating an edge-strengthened dilated holistic edge detection (ES-D-HED) segmentation network with a fine-tuned ResNet152V2 classification model. Unlike the conventional HED-based approaches, the pr

Barely three days after disclosure, attackers are widely chaining together CVE-2026-60137 and CVE-2026-63030 to lob exploit attempts against one of the largest attack surfaces on the Internet.

Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounting. While vision foundation models like SAM show remarkable zero-shot capabilities, they frequently fail in geospatial domains due to topological complexity, cropland texturing patterns, and a lack of physical scale awareness. In this work, we introduce Delineate Anything v2, a globally scalable foundation model designed specifically for wide-are

Large language models (LLMs) are increasingly adopted for network automation, yet their output quality and inference cost can vary substantially across LLM families. We present HuGLEN, a stepwise evaluation pipeline that uses an LLM-as-a-judge together with a small set of expert ratings to enable scalable and reproducible comparison of candidate LLMs, and to rank them using a quality efficiency score (QES). We demonstrate HuGLEN for translating outputs from an explainable artificial intelligence

Die EU-Kommission hat Leitlinien zur Kennzeichnung von KI-Inhalten verabschiedet. Ab August müssen Anbieter KI-generierte Inhalte transparent ausweisen.

Natural-language control offers a promising interface for unmanned aerial vehicles (UAVs), but directly applying self-hosted computer-use agents (SHCUAs) to UAV control introduces a structural mismatch. SHCUAs are designed for interactive host-side tool use, where delayed agent iterations are often acceptable. UAV control, however, is coupled with continuously changing physical states, strict timing constraints, safety risks, and security accountability. A stale, unauthorized, or tampered agent

Banks are beginning to move beyond AI models that only score, classify, or recommend. The next wave ...

Scientific Reasoning Graph Extraction (SRGE) aims to recover explicit links among observations, evidence, intermediate claims, and paper-level conclusions. LLMs can produce graph-like scientific explanations, but their outputs often mix malformed syntax, drifting edge labels, incorrectly oriented roots, and weak source anchors. We propose PEARL (Peircean Extraction via Abstraction and Repair Layer), a training-free framework that turns noisy LLM graph responses into auditable reasoning graphs an

Alternative legal services provider QuisLex today (20 July) unveiled a new advisory framework designed to help legal departments and law firms build the institutional capability needed to produce AI-assisted legal work that […] The post QuisLex launches AI capability framework as focus shifts from adoption to accountability appeared first on Legal IT Insider .

These EU AI Act provisions will start to apply on August 2 | Edition #307

La Comisión Europea publica las directrices sobre transparencia para sistemas de inteligencia artificial como los populares chatbots y para detectar ‘deepfakes’ que entrarán en vigor el 2 de agosto

Rapidly growing agetech industry has a significant opportunity to close trust gap, increase product adoption with increased transparency WASHINGTON, D.C. — (July 20, 2026) — The Future of Privacy Forum (FPF) — a global non-profit focused on data protection, AI, and emerging technologies — today released findings from a comprehensive March 2026 survey about how […]

Financial statements (FS) such as Balance Sheet (BS), Income Statement (IS) and Cash-flow Statement (CS) summarize the annual financial performance of a company. FS are widely used for evaluating corporate governance, credit appraisal, risk analysis, validate taxation, make investment decisions etc. Financial auditing is a complex and knowledge-intensive discipline whose one important aim is ensuring integrity, accuracy, fairness and absence of material misstatement in the published FS. Given th

Après l’amende de 150 millions d’euros infligée à Apple pour les modalités d’ATT, Le Figaro, L’Équipe et Adikteev réclament désormais réparation. Les trois entreprises estiment avoir subi un préjudice cumulé de 131,5 millions d’euros. Depuis iOS 14.5, livré en avril 2021, le dispositif de Transparence du suivi par les apps (ATT) a permis aux utilisateurs […]

Cyber-physical systems built on deterministic edge inference, such as on-vehicle flood detection for agricultural fields, produce structured decision logs that must be interpreted differently by heterogeneous stakeholders. Pairing such systems with large language models (LLMs) to generate stakeholder-specific reports introduces a tension: the generative layer is non-deterministic, while the edge plane must remain replayable and auditable. We propose an architectural pattern resting on two invari

Within Explainable Artificial Intelligence, mechanistic interpretability uses Sparse Autoencoders (SAEs) to extract more interpretable features from neural representations. However, assessing their monosemanticity, and thus explanation quality, remains challenging. Existing metrics require external concept labels or depend on pretrained embedding models, making them sensitive to encoder's geometry. We introduce the Tversky Monosemanticity Score (TMS), a label-free metric that operationalizes mon

SCAORA opposes mandatory AI-use disclosures for lawyers, calling them unworkable. It also flags hallucinations, opaque AI systems, weak accountability, and risks to judicial data. The post Supreme Court lawyers’ body opposes mandatory AI disclosure in draft AI regulations appeared first on MEDIANAMA .

Commission publishes guidelines on transparency obligations for providers and deployers of certain AI systems Anonymous (not verified) Mon, 07/20/2026 - 09:08 Today, the European Commission published guidelines to assist providers and deployers of artificial intelligence (AI) systems in meeting the AI Act's transparency obligations, which start to apply on 2 August 2026. Transparency obligations will help people recognise when they are interacting with AI or when content has been generated or al

How Soon Could Colleges Lose Loan Access Under New Accountability Metric? jessica.blake@… Mon, 07/20/2026 - 03:00 AM For most programs, data from the new test on student earnings will be released in 2027 and failing programs could face penalties in 2028. But some have been granted an extension that student advocates say is harmful. Byline(s) Jessica Blake

Guidelines on transparency obligations for providers and deployers of AI systems Anonymous (not verified) Mon, 07/20/2026 - 08:58 These guidelines define the scope of transparency obligations for providers and deployers of AI systems under article 50 of the AI Act. The AI Act follows a risk-based approach, classifying AI systems into four different risk categories, one of which is AI systems posing transparency risks that are subject to the obligations laid down in Article 50 of the AI Act . The

Modern machine learning (ML) pipelines depend heavily on third party libraries for graph compilation and hardware acceleration. While current practices audit data and model artifacts or rely on file integrity checks, the execution environment remains implicitly trusted. This blind spot enables active threats where a malicious runtime module interacts directly with live training and inference dynamics: exploiting this interaction allows the Trojan to support complex objectives that are challengin

arXiv:2607.15397v1 Announce Type: new Abstract: Electronic health record audit logs record timestamped actions through which clinical work is carried out. Generated as operational metadata, they now support research on clinician effort, patient outcomes, care-team coordination, and workflow structure. This Perspective explains that breadth by articulating audit logs as multi-axial event streams and drawing implications for representation learning, evaluation, and governance. Each logged action b

arXiv:2607.16130v1 Announce Type: new Abstract: AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way. Yet existing work on AI trustworthiness remains either too high-level to support lifecycle monitoring and reassessment or too narrowly metric-driven to connect with governance needs. We therefore propose a lightweig

arXiv:2607.15769v1 Announce Type: cross Abstract: Generative AI and coding agents are intensifying a central governance tension in open-source software (OSS): they scale contribution generation faster than maintainers can assess risk, evidence, and accountability. Existing responses improve agent-readability and traceability, but project rules must also organize contribution-specific risk, evidence, accountability, and review-gate states. We theorize this organizational arrangement as project-si

arXiv:2607.16010v1 Announce Type: cross Abstract: Governments are increasingly mandating that LLM-generated content carry watermarks. The EU AI Act calls for markings that are "sufficiently reliable and robust." California's SB 942 requires disclosure that is "permanent or extraordinarily difficult to remove." Both mandates rest on an untested assumption: that watermark detection yields evidence reliable enough for courts. This paper tests that assumption directly. We evaluate three representati

arXiv:2604.06381v2 Announce Type: replace-cross Abstract: This paper examines artificial intelligence (AI) companionship as a site where intimate relations are simultaneously produced, extracted from, and governed through datafied systems. Drawing on critical data studies and platform studies, we challenge prevailing narratives that locate harm in user psychology rather than platform architecture. Through in-depth interviews with 20 individuals who have AI companions, we address three questions: