Transparency

Die Medienanstalten stufen KI-generierte Antworten als eigene Inhalte ein und fordern Transparenz. Das DSA-Haftungsprivileg greift hier laut Gutachtern nicht.

Cutting-edge artificial intelligence models are deploying with more independence and less human oversight. Several state governments are trying to legislate transparency in their use.

Summary Researcher Dave Kuszmar discovered multiple systemic vulnerabilities that let him bypass LLM safety and obtain dangerous instructions . These exploits worked across nearly all major LLMs revealing an industry-wide security problem. Kuszmar calls for slowing deployment, increasing transparency , and large-scale research into LLM safety before further integrating these systems into society. On a fine bright afternoon last fall, my colleague Matthew Gore-Kormanik (or Zigula, as he prefers t

McConnell's hospitalization, Trump's health opacity, and now a senator's sudden death are forcing a GOP reckoning.

Settlement will drive down patients’ out-of-pocket costs, increase transparency and ensure community pharmacies are treated fairly The Federal Trade Commission secured a settlement agreement with one of the nation’s largest pharmacy benefit managers (PBMs) and its affiliated entities, marking yet another important victory in the Commission’s fight to lower healthcare costs for Americans. View Press Release

Community leaders and organizers rallied to demand accountability after an ICE agent fatally shot a motorist in Biddeford, Maine on Monday.

According to the recent European legislation, high-risk AI systems will have to adapt in order to comply with requirements related to specific areas, like risk management, data quality and governance, logging and traceability, technical documentation, transparency, human oversight, and accuracy, as outlined in the European Artificial Intelligence (AI) Act. As the standardisation process for AI is expected to remain iterative and, so far, there are no European standards on AI fully covering the c

arXiv:2607.11292v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed as conversational tutors, they risk institutionalizing systemic inequalities. This study presents a systematic API audit of four LLMs acting as history tutors, evaluating 1,800 responses regarding the 1989 Romanian Revolution across five student personas varying by ethnicity and socio-economic tier. We uncover four interconnected patterns of \emph{epistemic paternalism}: (1)~\textbf{Differen

arXiv:2607.11276v1 Announce Type: cross Abstract: Ensuring the quality of educational materials requires more than standard proofreading: textbooks must be audited for factual accuracy, domain-specific technical correctness, and linguistic quality simultaneously -- a task that general-purpose grammar checkers cannot address. We present \textbf{AI Textbook Auditor}, a modular multi-agent pipeline for automated quality assurance of educational materials across subject domains. The system accepts a

By Swagatam Sen, Founder & CEO, ControlOne Financial crime teams have been offered a choice for ...

Sparse autoencoders (SAEs) are the standard for decomposing superposed neural representations into interpretable features, and evaluation relies predominantly on correlational recovery metrics -- cosine similarity between ground-truth directions and decoder atoms. We show this conflates two distinct claims: decoder-geometry alignment and encoder-activation behavior. We reproduce the superposition phase diagram of Elhage et al. (2022), identifying a convergence artifact at high sparsity and an un

Explore practical strategies for improving Medicaid beneficiary engagement, streamlining exemption verification and strengthening audit readiness.

Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become increasingly recognized as a cornerstone of capable, transparent AI systems. Yet while LLMs have made significant progress across diverse real-world tasks, it is not yet clear when, how, or to what extent they can exhibit or be endowed with effective metacognitive abilities, nor how such abilities can be adapted to adv

Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding. Existing explainability efforts rely on textual rationales or sparse bounding boxes, which struggle to capture complex video dynamics such as occlusions and non-rigid deformations. We propose Evidence-Backed Video Question Answering (E-VQA), a novel task requiring models to jointly output a semantic answer and precise

Narmi, a leading digital banking platform provider for banks and credit unions, today announced the upcoming launch of AI Decision Assist, a new agentic AI capability designed to help financial institutions automate and accelerate account opening reviews while still maintaining control, transparency, and compliance.

Explainability has emerged as a critical requirement for AI-based systems, particularly in safety-critical and regulated domains. Although prior research has proposed frameworks, patterns, and user-centered approaches to support explainability, there is limited empirical understanding of how existing Requirements Engineering (RE) practices support explainability requirements across the RE lifecycle, especially in an industrial context. This paper reports early findings from an ongoing industry-b

Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams. These scams often span over multiple weeks or months, gradually build trust and request for money or sensitive information. Existing scam-detection systems mainly focus on isolated messages, which renders them inadequate against this evolving threat. This paper extends single-message phishing detection and presents an explainable agentic system for detecting sophisticated

Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams. These scams often span over multiple weeks or months, gradually build trust and request for money or sensitive information. Existing scam-detection systems mainly focus on isolated messages, which renders them inadequate against this evolving threat. This paper extends single-message phishing detection and presents an explainable agentic system for detecting sophisticated

Sollte die Bundesregierung die Pläne umsetzen, wird es für Bürger:innen und Presse noch schwieriger an staatliche Dokumente zu kommen. (Symbolbild) – Gemeinfrei-ähnlich freigegeben durch unsplash.com: Anastassia Anufrieva Damit hat Schwarz-Rot offenbar nicht gerechnet: Heftige Kritik am Angriff auf die staatliche Transparenz kommt nicht nur von der Opposition, sondern aus der Koalition selbst. Dazu erreicht eine Petition gegen das Vorhaben bemerkenswerten Zulauf.

This article was originally published by IAPP linked here. Organizations using scraped data for AI training should prepare for heightened expectations around data minimization, transparency and accountability. On 7 July, the European Data Protection Board approved “Guidelines on web scraping in the context of generative AI.” Perhaps not surprisingly, the EDPB considers that web scraping [...] The post Thought for the week: Web scraping for generative AI is subject to the GDPR appeared first on C

Distributional reinforcement learning agents learn full return distributions that are increasingly read at face value: for interpretability, risk-sensitive control, and safety monitoring. We ask a question theory anticipates but that has not been measured directly: are the risk claims of a trained distributional agent true? Our audit combines a decision-relevant screening metric (the excess Wasserstein gap between the top two actions, which equals the mass by which first-order stochastic dominan

Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become increasingly recognized as a cornerstone of capable, transparent AI systems. Yet while LLMs have made significant progress across diverse real-world tasks, it is not yet clear when, how, or to what extent they can exhibit or be endowed with effective metacognitive abilities, nor how such abilities can be adapted to adv

Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding. Existing explainability efforts rely on textual rationales or sparse bounding boxes, which struggle to capture complex video dynamics such as occlusions and non-rigid deformations. We propose Evidence-Backed Video Question Answering (E-VQA), a novel task requiring models to jointly output a semantic answer and precise

* Lindsey Graham died over the weekend, removing a key Trump ally from the Judiciary Committee in advance of Todd Blanche's already controversial nomination hearings. [ PBS ] * Civil rights coalition calls for Senate to reject Blanche. [ Ms ] * Audit reveals the broken California alternative bar exam process. [ ABA Journal ] * Judges embark on whistlestop tour to explain the increasing threats against the judiciary. [ Washington Post ] * DOJ opens investigation into UAW president. If only a work

As Large Language Models (LLMs) are increasingly deployed as conversational tutors, they risk institutionalizing systemic inequalities. This study presents a systematic API audit of four LLMs acting as history tutors, evaluating 1,800 responses regarding the 1989 Romanian Revolution across five student personas varying by ethnicity and socio-economic tier. We uncover four interconnected patterns of \emph{epistemic paternalism}: (1)~\textbf{Differential Refusal}, where safety-aligned models block

SPUR is publisher‑run and fixated on one thing: turning AI’s use of their content from opaque scraping into a transparent, usage‑based licensing system they control.

Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.

Directly Responsible Individuals (DRI) I went looking for a definition of "Directly Responsible Individuals" and the best I found was in the GitLab handbook. Apparently the term originated at Apple, where it's used to describe the person who is "ultimately accountable for the success or failure of a specific project, initiative, or activity". I've been thinking about this term recently in the context of LLM-powered agents and how they fit into human organizations. I don't think an agent should e

Independently trained neural networks have no shared neuron-index reference frame, so comparing them requires accounting for coordinate freedom. Neural Collapse sharpens this problem: networks converge toward a shared, low-dimensional geometry, raising the question of whether trajectory-specific functional variation remains distinguishable after convergence. We distinguish three claims - detectability, transplantability, and causal persistence - and address the first. Using five independently tr

A majority of U.S. employees now want an AI sovereign wealth fund to hold corporations more accountable, according to a recent survey, as tech layoffs rise.

Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback. Translating this capability to medical imaging remains difficult because each task imposes modality-specific experimentation and strict requirements for validation protocols and prediction artifacts. Here we introduce AMID, an autonomous multi-agent framework for medical imaging model development. AMID first proposes Data-Conditio

Autonomous CLI agents can now execute hundreds of actions across multi-hour sessions: writing code, executing shell commands, browsing the web, and managing cloud infrastructure, all with minimal human oversight. Does greater autonomy invite greater risk? We introduce ANCHOR, an automated auditing framework that stress-tests CLI agents on illegal tasks grounded in public US court cases. ANCHOR deploys an auditor agent fine-tuned on dark personality data using supervised and reinforcement fine tu

Vision Transformers (ViTs) are difficult to interpret because current methods of relevance propagation and attention flow do not fully consider some key architectural features, such as the uneven importance of attention heads and residual connections. Prior approaches typically assume uniform importance across attention heads; furthermore, they model skip connections as identity paths, leading to inaccurate relevance attribution. To address these issues, we introduce GradSkip, a novel relevance

Five frames for seeing how AI is reshaping decisions, accountability, and knowledge.

Recent advances in large-scale multimodal models have drivenremarkable progress in vision-language tasks; however, comprehensiveomni-modal understanding remains under-explored, largely due to thescarcity of datasets with rich, explicitly aligned auditory cues. To bridgethis gap, we present AVDC (Audio-Visual Decoupled Captions), a large-scaledataset designed to disentangle visual and auditory semantics. Specifi-cally, we propose an automated pipeline that leverages off-the-shelf mod-els to annot

Large language model agents increasingly store reusable procedures outside the model. These reusable procedures are often called \emph{skills}: they may be code functions, natural-language instructions, SKILL.md packages, workflow graphs, or learned adapters that a future agent can retrieve and invoke. This taxonomy-driven survey asks how such skill libraries change over time. Across a $124$-paper $2023$--$2026$ audit set, we synthesize dynamic skill systems as \emph{lifecycle-managed, verified,

Alors que l’intelligence artificielle permet désormais de créer des morceaux entiers, parfois difficiles à distinguer de productions humaines, des organisations professionnelles plaident pour davantage de transparence sur l’origine de ces titres.

Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost: existing Explainable AI (XAI) methods suffer from a persistent accuracy-explainability trade-off. We argue that this trade-off is not fundamental, but an artifact of treating explanation and prediction as separate objectives; when properly coupled, they become complementary, so that equipping a model to explain itself improves, rather than degrades, it

WASHINGTON, D.C. — Last night, federal lawmakers introduced the People-First Chatbot Act, a clear framework to make chatbots safer for everyone and address the harms caused by AI chatbots that were rushed into the public’s hands with little oversight or transparency. EPIC applauds Reps. Valerie Foushee and Greg Casar for sponsoring this important bill.

WASHINGTON, D.C. — Last night, federal lawmakers introduced the People-First Chatbot Act, a clear framework to make chatbots safer for everyone and address the harms caused by AI chatbots that were rushed into the public’s hands with little oversight or transparency. EPIC applauds Reps. Valerie Foushee and Greg Casar for sponsoring this important bill. This legislation was crafted to ensure that the companies operating AI chatbots provide users with clear disclosures, material safeguards, and co