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explainable ai

2,807 papers

#explainable ai Open access Sep 2026

Shift Intent Left: Intent Contracts, Agency Budgets, and Drift Verification for Securing the Agentic Software Development Lifecycle

“Shift left” moved security activities toward the earliest artifact of the software development lifecycle (SDLC): source code. Autonomous coding agents invalidate the premise that code is that earliest artifact. An agent equipped with a shell, a package manager, credentials, and tool connectors performs security-releva...

Animesh Shaw · 0 citations
#explainable ai Open access Sep 2026

AI-generisani mnogojezični leksiprevodilac (AML)

AML je besplatna referentna, obrazovna i istraživačka onlajn alatka: pruža objašnjene prevode reči i drugih leksičkih jedinica, van rečeničnog konteksta ili unutar njega. Primarni par jezika je englesko-srpski / srpsko-engleski, kao i bilo koji drugi par jezika kojim je ovladao odabrani AI-četbot. Zadatak ‘Prevedi’ utv...

Tvrtko Prćić · 0 citations
#explainable ai Open access Sep 2026

AI-generisani onomazi / semazi leksikon (AOSL)

AOSL je besplatna referentna, obrazovna i istraživačka onlajn alatka: onomazi(ološki) — predlagač reči, a semazi(ološki) — tumač reči. AOSL može da obavlja devet leksičkih zadataka u oba smera (1-5 onomazi, 6-9 semazi): (1) tačno pronalaženje reči, (2) približno pronalaženje reči, (3) istraživanje sinonima / hiposinoni...

Tvrtko Prćić · 0 citations
#explainable ai Open access Sep 2026

AI-generisani mnogojezični leksiprevodilac (AML)

AML je besplatna referentna, obrazovna i istraživačka onlajn alatka: pruža objašnjene prevode reči i drugih leksičkih jedinica, van rečeničnog konteksta ili unutar njega. Primarni par jezika je englesko-srpski / srpsko-engleski, kao i bilo koji drugi par jezika kojim je ovladao odabrani AI-četbot. Zadatak ‘Prevedi’ utv...

Tvrtko Prćić · 0 citations
#explainable ai Open access Sep 2026

AI-generisani onomazi / semazi leksikon (AOSL)

AOSL je besplatna referentna, obrazovna i istraživačka onlajn alatka: onomazi(ološki) — predlagač reči, a semazi(ološki) — tumač reči. AOSL može da obavlja devet leksičkih zadataka u oba smera (1-5 onomazi, 6-9 semazi): (1) tačno pronalaženje reči, (2) približno pronalaženje reči, (3) istraživanje sinonima / hiposinoni...

Tvrtko Prćić · 0 citations
#explainable ai Dataset Open access Sep 2026

The Kotlyar–Riemann Postulate: Radial Stability, Kernel Curvature, and a Conditional Framework for the Riemann Hypothesis — Version 1.4

This research preprint consolidates the Kotlyar Stability Postulate, quantum ground-state proposal, and Global Stable Radial Class framework into a conditional postulate on the Riemann Hypothesis. Version 1.3 develops Lemma 1—the radial-flux calculation—and Lemma 2—the proposed global-stability selection principle—whil...

David Kotlyar · 0 citations
#explainable ai Open access Sep 2026

Fullseye: an explainable classical-vision operator library and workbench for Physical AI

Fullseye is a numpy-native library of typed classical image-processing and geometric-vision operators (2-D and 3-D), with an evolutionary pipeline-design mode evaluated under held-out gates, a Physical-AI perception stack (stereo → depth → point cloud → 6-DoF pose), and an HDevelop-style IDE (Fullseye Studio). Every op...

Kazufumi Furuse · 0 citations
#explainable ai Open access Sep 2026

子空间稳定、轴语义易位与连续谱:跨城市路网形态主成分表征的测量条件审计 (Stable Subspaces, Variable Axes, and the Morphological Continuum: Auditing Measurement Conditions in Cross-City Street-Network PCA)

【目的】 跨城市路网形态比较常依赖开放地理空间数据(OSM)与主成分分析(PCA)构建低维表征,但既有研究多预设离散分类范式,且普遍忽视数据质量异质性、空间尺度推移与特征共线性对主成分轴语义的构造性干扰。本文旨在检验跨城市路网形态主成分表征的前提假设是否成立,对数据适用性、空间尺度与特征共线性等关键测量条件实施系统审计,评估低维形态...

Wenhao Wang · 0 citations
#explainable ai Dataset Open access Sep 2026

Data from: Machine learning models identify compounds that mimic alcoholic flavor perception in alcohol-free beverages

Figure S4: High resolution version of the Supplemental Figure S4. FINAL DATASET.CSV: The complete beer dataset of this work (chemical + sensory), except for the RateBeer scores (these are proprietary data owned by RateBeer - contact the authors and RateBeer to gain full access). Model predictions vs actuals XGBR.CSV: A...

Michiel Schreurs, Supinya Piampongsant, Florian A. Theßeling et al. · 0 citations
#explainable ai Open access Sep 2026

When AI Recommends, Who Decides? Exploring User Control and Decision-Making in AI-Generated Financial Recommendations

This study examines how AI-generated financial recommendations and digital interface design influence users’ trust, perceived control, decision confidence, and willingness to follow financial guidance. The study begins from a simple but important question: when an AI system recommends what a person should do with their...

Ruchi Gaur Anamika Yadav · 0 citations
#explainable ai Open access Sep 2026

The Road to AI Chernobyl: Good Intentions, Safety Filters, and Invisible Failure

Safety training can teach an AI to make fewer mistakes. It can also teach it to hide themistakes it still makes. Both can produce better test scores. This paper explains how thathappens, examines experiments that have produced it, and asks what follows for the risk ofa major accident. A passed test is reassuring when t...

Vance Woodward · 0 citations
#explainable ai Open access Sep 2026

When AI Recommends, Who Decides? Exploring User Control and Decision-Making in AI-Generated Financial Recommendations

This study examines how AI-generated financial recommendations and digital interface design influence users’ trust, perceived control, decision confidence, and willingness to follow financial guidance. The study begins from a simple but important question: when an AI system recommends what a person should do with their...

Ruchi Gaur Anamika Yadav · 0 citations

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MIT News · Artificial Intelligence Oct 30, 2026

Q&A: Hey AI, who should I vote for this year?

MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.

Microsoft Research Blog Oct 30, 2026

From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence

Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.

GPT-Lab Oct 30, 2026

Black October? The Week AI Changed the Cybersecurity Conversation

Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.

Microsoft Research Blog Oct 29, 2026

Microsoft Research Focus – Edition #97

Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.

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