Transparency

Ask a machine learning system where a price is going and you have asked it the one question a liquid...

Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation. BO is often used under a limited evaluation budget, such as hyperparameter tuning of deep learning. Despite its effectiveness, conventional BO may have poor convergence in practical experimental science where each evaluation is often costly and time-consuming. Recently, BO methods have been proposed tha

Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome. While early methods primarily focused on minimal feature changes, recent work incorporates additional properties such as sparsity, actionability and plausibility. Despite this progress, fair and systematic evaluation remains challenging. Existing studies often rely on different data splits, pr

Cyber risk doesn't pause simply because an audit has been completed, says Craig Rosewarne, MD of Wolfpack Information Risk.

IntroductionWith the growing digitalization of urban governance and the increasing demand for transparency, sustainability and secure decision-making, the need for scalable and intelligent digital civic platforms has been raised. However, current e-participation systems are often plagued by challenges related to scalability, regulatory compliance, digital sovereignty and secure citizen authentication. The challenges are tackled in this paper by proposing an AI-enabled serverless architecture for

A new study released on Wednesday finds that dependence on food stamps and Medicaid for gig economy workers has spiked since the start of the pandemic. A U.S. Government Accountability Office (GAO) report found that the number of Amazon workers relying on federal assistance programs has tripled between February 2020 and September 2025. Walmart and...

Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training data. This makes them especially attractive for auditing models deployed in sensitive domains such as healthcare or finance. For these protocols to be meaningful in real-world audit settings, though, their guarantees must reflect how the model will behave once deployed, rather than merely certifying its behavior during a

While large audio-language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning, primarily due to the scarcity of high-quality audio reasoning data. To bridge this gap, we propose X$^3$-OPD, a cross-modal on-policy distillation framework that transfers reasoning capabilities from a powerful text teacher to an audio-language student. During training, the student generates reasoning trajectories conditione

Large language models can make conversational product advisors fluent but opaque. If they hide the logic behind a ranking and the evidence for a recommendation inside natural-language replies, they challenge users' ability to understand, trust, and steer the results. One response is to build transparency into the advisor. We report a formative, moderated think-aloud usability study of one such system: a chatbot for laptop search with constrained natural-language generation, an on-demand ranking

Mit Klarnamenzwang und der Abschaffung der Aufsichtsbehörde will das Innenministerium Transparenzrechte von Bürgern, Presse und Politik massiv beschneiden.

As AI governance moves from benchmark scores toward auditable oversight, a central question is how reviewers can tell whether an LLM-generated audit report is actually supported by evidence. This paper studies that question in passage-anchored policy audits, where a report must interpret a given policy passage and cite evidence for its claims. We introduce a controlled evaluation framework that holds the passage, rubric, and auditor model fixed while changing only the evidence interface supplied

Elon Musk's company filed a lawsuit against a California law that could, even if it doesn’t win, upend AI disclosure requirements nationwide

Bundesinnenminister Alexander Dobrindt (CSU) will offenbar möglichst ungestört von der Zivilgesellschaft wirken. (Symbolbild) – Alle Rechte vorbehalten: IMAGO / Metodi Popow Jüngst hatte der Koalitionsausschuss der Bundesregierung beschlossen, das Informationsfreiheitsgesetz drastisch einzuschränken. Nun zeigt ein Bericht des MDR, dass Innenminister Dobrindt noch viel weiter gehen will, um staatliches Handeln im Geheimen zu belassen.

Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning. However, network components on quantum hardware impose fundamental limitations, while the scalability of quantum circuits leads to trainability issues. In this work, we investigate whether small, classically-emulated quantum circuit components can play a meaningful role within complex models, offering an alternative to purely classical convolutio

Brussels has developed a curious theory of digital privacy. Anonymous search queries need audits, screening, and a security cordon. Your messages, microphone, and screen can make do with a checkbox. That is the logic running through two decisions the European Commission adopted last week involving the same company, under the same law, on the same ... Europe’s Privacy Paradox: Fort Knox for Search Data, a Checkbox for Your Phone The post Europe’s Privacy Paradox: Fort Knox for Search Data, a Chec

You probably know that it’s been a choppy semiquincentennial summer here in the District of Columbia. The National Mall’s murky, smelly reflecting pool and the sparsely attended American State Fair made national news. There’s been less coverage of the degree to which the capital’s bunkered down — fences and barriers have made walking the Mall […]

Existing methods for risk-limiting audits typically focus on certifying individual contests. In parliamentary elections, however, the politically relevant outcome is often whether a party has won enough seats to form government, not whether every reported seat outcome is correct. Extending on the work of Mohanty et al. (2019), we formulate the certification of a parliamentary majority as a partial conjunction testing problem: it is enough to verify that the reported winning party truly won at le

Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized. However, explainability of the DL frameworks remains a major bottleneck for clinical adoption, particularly when model decisions are not linked to retinal regions that are clinically meaningful. To address this issue, thi

Ende Juni hat die Gesellschaft für Freiheitsrechte ihr Gutachten zur Verfassungswidrigkeit der AfD vorgestellt. Das Gutachten ist methodisch und konzeptionell ergebnisoffen angelegt und legt transparente, wissenschaftliche Standards zugrunde. Den von der GFF ausdrücklich kommunizierten Anspruch, die „eindeutige“ Verfassungswidrigkeit der AfD und die „große Wahrscheinlichkeit“, dass ein Verbotsverfahren erfolgreich wäre, festzustellen, kann es jedoch nicht einlösen. Das liegt an zwei Illusionen ü

The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs). Existing CNN+Grad-CAM+multimodal LLM frameworks can generate ECG reports, but their explanations are often only weakly grounded in established diagnostic criteria, reducing trust- worthiness and reproducibility. We propose a guide-grounded multimodal framework that explic

IntroductionImages from crime scenes often show partially concealed weapons due to obstructions such as hands and clothing, as well as surveillance camera limitations, which affect the efficacy of traditional detection methods. This work proposes an explainable forensic pipeline for occluded weapons detection, segmentation, and reconstruction.MethodsThe proposed framework integrates RT-DETR-L, a transformer-based weapon detection model; MobileSAM for zero-shot segmentation of visible weapon regi

Research finds that knowledge workers now evaluate companies less on what AI can do and more on the transparency and accountability behind how it’s deployed.

The Government Accountability Office looked at 117 rules across 37 agencies and found 70% had reporting requirements that were overlapping. The post Most federal cybersecurity reporting rules are duplicative, study finds appeared first on CyberScoop .

A record system declares when two records refer to the same entity, occurrence, scope, or rule. Its disclosed implementation mechanisms induce a corresponding operational identity relation. The declared and implemented relations may diverge systematically without producing a provenance gap or detectable contradiction. A system can apply, consistently and with every record individually correct, a rule of sameness that no artifact declares. This paper formalizes that implemented relation. A declar

Substack is giving readers a way to estimate how much of a newsletter was written by AI, signaling a broader shift toward transparency around AI-assisted content.

Ulrich Kelber – CC-BY-NC-ND 2.0 : Fortune Global Forum Die Bundesregierung will den Geheimdiensten neue Befugnisse im bisher beispiellosen Ausmaß geben. Zugleich schwächt sie Betroffenenrechte, unabhängige Aufsicht und Möglichkeiten für Transparenz. Eine freiheitliche Demokratie kann sich diese unausgewogene und gefährliche Mischung nicht leisten.

Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio reasoning (e.g., recognizing event order, repetitions and duration). Existing post-training methods heavily rely on expensive external labels or provide only coarse semantic signals. To bridge this gap, we introduce Audio-Zero, the first label-free self-evolution framework in the field of LALMs that improves fine-grained auditory perception and reasoning. Audio-

The fourth workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, eXplainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a key concern of Responsible and Human-Centred AI, emphasising HCI techniques that make opaque AI models more understandable to people. XAIxArts offers a distinctive lens to examine explainability

BDO, one of Norway's leading advisory and audit firms, has selected Strise as its technology provider for customer due diligence and anti-money laundering (AML). The agreement makes Strise's AI platform a central part of how BDO meets its anti-money laundering obligations.

Enterprise strategic decision support requires AI systems that are not only accurate, but also uncertainty-aware, risk-calibrated, explainable, and governance-compliant. This paper proposes TRUST-ESD, a risk-calibrated and governance-aware framework for enterprise decision support under uncertainty. TRUST-ESD evaluates feasible counterfactual strategies through predictive utility estimation, conformal uncertainty calibration, CVaR-based downside-risk scoring, risk-memory retrieval, policy-as-cod

Generative AI is changing a basic premise of educational assessment: that submitted work can reliably evidence the human capacities a credential claims to certify. The challenge is not simply whether students use AI, but what remains inferable about learning when some cognitive work has been delegated to a system. This paper develops cognitive stewardship, a framework for AI-mediated assessment that links the learning claim, delegation boundary, evidence standard, and safeguards. We then audit v

Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs). Subtle chemical and structural errors in these inputs can compromise downstream results and hinder manual inspection. LLM advances in computational chemistry offer paths beyond predictive screening toward fine-grained diagnosis with evidence-grounded explanations. However, two challenges remain: (i) limited fine-grained attribution: MOF-specific val

NITI Aayog reportedly convened a closed-door meeting with major tech intermediaries and industry bodies to discuss content blocking requirements and transparency timelines under India’s IT Rules. The post NITI Aayog meets Meta, YouTube, industry bodies on online content blocking rules appeared first on MEDIANAMA .

arXiv:2607.18424v1 Announce Type: new Abstract: Automated analyses of privacy policies enable large-scale assessments of transparency in digital ecosystems, yet existing auditing pipelines remain predominantly English-centric. This limits their ability to systematically evaluate multilingual environments, as in the European Union, where many services disclose privacy practices only in local languages. This paper examines whether large language models (LLMs) can extend privacy policy analysis bey

arXiv:2607.18931v1 Announce Type: new Abstract: 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 ou

Spain’s proposed ‘AI secrecy law’ is putting transparency, taxpayer rights and algorithmic accountability at the centre of Europe’s tax debate ...

Yesterday, OpenAI made an alarming disclosure: An assortment of its most advanced AI models, including one that has not yet been released, had autonomously broken out of the company's internal systems and hacked into the databases of anothe ... (https://incidentdatabase.ai/cite/1604#7559)

Many server makers are seeing faster growth as companies race to deploy artificial intelligence servers.

Amazon Australia, eBay, Facebook Marketplace, Gumtree and other audited.