9 papers
Fact-Check Your Information (FYI): A Design Probe to Understand How People Actually Fact-Check Data-Driven Articles
Nguyen-Truong Thinh, Yuxuan Du, Phongsakon Mark Konrad +1
Data-driven journalism and policy reports frequently rely on statements grounded in statistical evidence, referred to as data claims. Verifying such a claim requires connecting it…
Routing Subspaces: Auditing Evaluation-to-Deployment Mismatch in Fine-Tuned Language Models
Phongsakon Mark Konrad, Toygar Tanyel, Serkan Ayvaz
Safety evaluations often assume that behavior observed during testing reflects behavior in ordinary use, but fine-tuning can break this assumption. A checkpoint can appear fixed un…
The Open-Box Fallacy: Why AI Deployment Needs a Calibrated Verification Regime
Phongsakon Mark Konrad, Tim Lukas Adam, Ane Cathrine Holst Merrild +4
AI deployment in sensitive domains such as health care, credit, employment, and criminal justice is often treated as unsafe to authorize until model internals can be explained. Thi…
Acceptance Cards:A Four-Diagnostic Standard for Safe Fine-Tuning Defense Claims
Phongsakon Mark Konrad, Toygar Tanyel, Serkan Ayvaz
Safe fine-tuning defenses are often endorsed on the basis of a held-out gap reduction, but the same reduction can come from sampling noise, subject artifacts, capability loss, or a…
CAKE: Cloud Architecture Knowledge Evaluation of Large Language Models
Tim Lukas Adam, Phongsakon Mark Konrad, Riccardo Terrenzi +4
In today's software architecture, large language models (LLMs) serve as software architecture co-pilots. However, no benchmark currently exists to evaluate large language models' a…
Non-Destructive Prediction of Fruit Ripeness and Firmness Using Hyperspectral Imaging and Lightweight Machine Learning Models
Phongsakon Mark Konrad, Casper Kunstmann-Olsen, Jacek Fiutowski +1
Post-harvest fruit quality assessment is essential for reducing food waste, yet reliable non-destructive methods typically depend on expensive hyperspectral cameras and computation…