From the 2 of 8 linked papers with an AI index.
8 papers
AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanations
Javier Irigoyen, Roberto Daza, Francisco Jurado +5
The paper introduces AIriskEval-edu Demo, a platform that audits the pedagogical quality of K-12 instructional explanations by evaluating five risk dimensions and providing binary…
CrimeNER Demo: Named-Entity Recognition in the Crime Domain
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +7
The paper introduces CrimeNER Demo, an AI-driven platform that extracts and classifies crime-related entities from documents using pretrained and user‑fine‑tuned NER models.
Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +7
The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this information can be interpreted as a Named…
Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond
Javier Irigoyen, Roberto Daza, Aythami Morales +5
The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this impera…
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
Javier Irigoyen, Roberto Daza, Francisco Jurado +5
This work introduces AIriskEval-edu-db2, a new dataset designed to train and evaluate auditors based on LLMs for an explainable pedagogical risk assessment in instructional content…
Auditing Training Data in Domain-adapted LLMs: LoRA-MINT
Gonzalo Mancera, Daniel DeAlcala, Aythami Morales +3
We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Natural Language Processing (NLP…