Transparency

Background: Summary of findings (SoF) tables are widely used in systematic reviews and clinical practice guidelines to present evidence about health care interventions in a concise and transparent format. Although developed to improve accessibility and interpretation of evidence, previous studies have shown that users often experience difficulties understanding statistical information, certainty ratings, and the relationships between outcomes and treatment effects. Limited research has explored

Oura Rings, Garmin GPS fitness watches, Apple Watches, Whoop bands—every year, more and more tech devices are promising to monitor our health and fitness, guide us toward healthier living, and provide useful health metrics to take to our doctors. But few of these tools provide the sorts of privacy and security promises we demand from all technology, let alone tech that captures personal health data. It’s time they step up and start providing transparency reports and stronger encryption options.

The rapid development of Large Language Models (LLMs) and Artificial Intelligent (AI) powered autonomous agents has fundamentally changed the existing forms of software governance. In spite of the rigorous standards of transparency and account ability required according to the international frameworks such as the European Union's AI Act, there is a considerable gap between theory and reality. The present study discusses the inherent drawbacks of currently utilized platforms for LLM evaluation, m

Background: Large language models (LLMs) are increasingly embedded in conversational agents for cardiometabolic care. These systems could support self-management, but their behavior change content, delivery mechanisms, and implementation transparency are poorly understood. Objective: This scoping review mapped behavior change techniques (BCTs) used in LLM-driven conversational agents for cardiometabolic prevention and management, described how these techniques are delivered across static, rule-b

Elon Musk, the billionaire owner of X, wants to make the social network one of the most transparent major technology The post Elon Musk: “We will make the entire codebase of X open source, with no exceptions.” appeared first on The New Stack .

Elon Musk a promis, sur X, de publier l'intégralité du code source de la plateforme une fois une revue de sécurité achevée. Mais sa précédente promesse de transparence, plus limitée, montre déjà les limites de l'exercice.

Whistleblowers are a key safeguard against organizational wrongdoing, but the threat of retaliation deters reporting. Existing whistleblower-protection proposals lack formal privacy guarantees, and existing differential privacy mechanisms do not directly target the natural threat model -- one in which the audited organization itself observes auditor selection decisions and uses them to identify reporters. We formalize protection against a strong-adversary threat model as per-report $(0, δ)$-diff

The bill requires full price transparency, forces anyone advertising 50 or more tickets a year for resale to register with the District. Source

Attorney General nominee Todd Blanche's prosecutions of Comey and others reveal a pattern of prosecutorial sycophancy — charges brought to please Trump. The post To Audition for the Role of Attorney General, Blanche Is Prosecuting to Please appeared first on Just Security .

Background and Objective: Deep video models estimate left-ventricular ejection fraction (EF) from echocardiography with near-expert accuracy, and post-hoc attribution (Chefer relevance for transformers, Grad-CAM for CNNs) is increasingly used to certify that models "look at the right place." Yet whether these explanations are faithful both spatially and temporally is unaudited. Because EF is defined by the end-systolic (ES) and end-diastolic (ED) frames, a faithful explanation must localize the

Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios. However, it is mostly based on user studies, thus targeting the needs of a specific audience and lacking shared evaluation metrics. On the other hand, logic-based approaches within eXplainable Artificial Intelligence (XAI) provide compact, human-readable abstractions of decision-making. However, the syste

The West African country advanced rules to force organizations to disclose cyberattacks, joining other nations in a shift to mandated transparency.

Digital video ad spend continues to climb, but buyers say bigger budgets bring tougher questions about inventory quality, supply chain transparency.

arXiv:2607.11918v1 Announce Type: cross Abstract: Dual submissions, in which identical or substantially similar papers are simultaneously submitted to one or more archival venues, without cross-citation or disclosure, are a growing problem for the AAAI Conference and other scientific publication venues. These submissions increase the burden on the peer-review system and pollute the scientific record. As part of the AAAI-26 review process, we (conference organizers) compared AAAI main-track submi

Coherent decision-making and internal accountability are critical to meeting this manic moment Anthony Albanese promises fast-track approvals for datacentres to shore up AI investment The University of Sydney was the natural setting for Anthony Albanese to lay out his vision for how Australia should confront the profound economic and social challenges posed by so-called artificial intelligence technology. His time around the jacaranda and sandstone in the early 80s was a seminal marker in the fu

Graph-based learning and explainable artificial intelligence (XAI) are increasingly used to improve both predictive performance and transparency in financial risk modelling. This paper presents a systematic literature review of AI and machine learning approaches for credit risk assessment and fraud detection, with specific attention to graph-based methods and explainable frameworks. Following a PRISMA-guided methodology, 149 studies published between 2015 and 2025 were analysed across multiple a

Turns out accountability was always something for other people. The post Amy Coney Barrett Then: ‘Read The Opinion!’ ACB Now: Inappropriate To Expect Supreme Court To Explain Decisions appeared first on Above the Law .

Patients with no other options are watching an agency turn on its own scientists — and the trust that took a century to build is draining out in two years.

Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path be

The persuasive power of data visualizations can go awry: for instance, in an explainable AI (XAI) context, visualizations can produce over-trust of predictive models. In this paper, we use a crowdsourced study to show that providing accurate (but superfluous or irrelevant) data in a model explanation can, in fact, result in unjustified trust and other positive beliefs about a model, even when the model is patently discriminatory and unfair. Our results suggest that XAI designers and developers n

Recommender-system research for Vietnamese remains limited by the absence of a public, well-documented hotel interaction resource. Building such a resource is challenging for three reasons: cross-platform hotel names must be reconciled before interactions are comparable; quality must be audited with reproducible metrics rather than ad hoc cleaning; and public release must preserve privacy while remaining benchmarkable under realistic cold-start conditions. We introduce ViHoRec, a quality-control

Publication date: June 2026 Source: Government Information Quarterly, Volume 43, Issue 2 Author(s): Shangrui Wang, Yuanmeng Zhang, Zhenming Huang, Zheng Liang

Publication date: June 2026 Source: Government Information Quarterly, Volume 43, Issue 2 Author(s): Shangrui Wang, Yuanmeng Zhang, Yiming Xiao, Zheng Liang

Publication date: June 2026 Source: Government Information Quarterly, Volume 43, Issue 2 Author(s): Jamil Civitarese, Gregory Michener, Octavio Amorim Neto

Publication date: June 2026 Source: Government Information Quarterly, Volume 43, Issue 2 Author(s): Hung-Yi Hsu

Publication date: October–November 2026 Source: International Journal of Human-Computer Studies, Volume 215 Author(s): Min Cai, Kuangyong Gao, Ziling Ji, Xueqi Xu

Publication date: October–November 2026 Source: International Journal of Human-Computer Studies, Volume 215 Author(s): Davide Piras, Alberto Pes, Diego Reforgiato Recupero, Giuseppe Scarpi

Publication date: August 2026 Source: Technology in Society, Volume 87 Author(s): Pedro C. Magalhães, Sveinung Arnesen, Christoph Kern, Pascal D. Koenig, Daniel S. Schiff, Tom R. Tyler

Publication date: November 2026 Source: Computers in Human Behavior, Volume 184 Author(s): Khanh Duy Phan, Bao Quoc Truong-Dinh

Publication date: Available online 11 July 2026 Source: Computers in Human Behavior Author(s): Yuya Shibuya, Yair Amichai-Hamburger

Publication date: August 2026 Source: Technology in Society, Volume 87 Author(s): Susanne Frennert, Björn Fischer, Günter Alce

Publication date: August 2026 Source: Technology in Society, Volume 87 Author(s): Carlotta Rigotti, Daniel Alves Fernandes, Antoni Mut Piña, Eduard Fosch-Villaronga

Publication date: August 2026 Source: Technology in Society, Volume 87 Author(s): Hamide Özyürek, Karahan Kara, Galip Cihan Yalçın, Zeynep Baysal, Ufuk Türen, Vladimir Simic, Mustafa Polat, Dragan Pamucar

Publication date: December 2026 Source: Computers and Education: Artificial Intelligence, Volume 11 Author(s): William Villegas-Ch, Alexandra Maldonado Navarro, Jaime Govea, Joselin Garcia-Ortiz, Diego Buenaño-Fernandez

Publication date: Available online 11 July 2026 Source: Computers and Education: Artificial Intelligence Author(s): Edmund Evangelista, Syed M.Salman Bukhari

Publication date: September 2026 Source: Technological Forecasting and Social Change, Volume 230 Author(s): Lin Wang, Fengqin Liu

Publication date: September 2026 Source: Technological Forecasting and Social Change, Volume 230 Author(s): Min Hong, Jingqi Shi, Manjiang Xing, Yuzhi Zhong

Publication date: Available online 10 June 2026 Source: Technological Forecasting and Social Change Author(s): Min Hong, Jingqi Shi, Manjiang Xing, Yuzhi Zhong

Call for CRAFT proposals - Critiquing and Rethinking Accountability, Fairness, and Transparency - ACM FAccT 2026 [Montreal] [Jun 25, 2026 - Jun 28, 2026]