Renata De Paris

Abriel K. Moraes, Gabriel S. M. Dias, Vitor L. Fabris et al.

The Consolidation of Labor Laws (CLT) serves as the primary legal framework governing labor relations in Brazil, ensuring essential protections for workers. However, its complexity creates challenges for Human Resources (HR) professionals in navigating regulations and ensuring compliance. Traditional methods for addressing labor law inquiries often lead to inefficiencies, delays, and inconsistencies. To enhance the accuracy and efficiency of legal question-answering (Q&A), a multi-agent system powered by Large Language Models (LLMs) is introduced. This approach employs specialized agents to address distinct aspects of employment law while integrating Retrieval-Augmented Generation (RAG) to enhance contextual relevance. Implemented using CrewAI, the system enables cooperative agent interactions, ensuring response validation and reducing misinformation. The effectiveness of this framework is evaluated through a comparison with a baseline RAG pipeline utilizing a single LLM, using automated metrics such as BLEU, LLM-as-judge evaluations, and expert human assessments. Results indicate that the multi-agent approach improves response coherence and correctness, providing a more reliable and efficient solution for HR professionals. This study contributes to AI-driven legal assistance by demonstrating the potential of multi-agent LLM architectures in improving labor law compliance and streamlining HR operations.

Matteus Vargas Simão da Silva, Rodrigo Reis Arrais, Jhessica Victoria Santos da Silva et al.

Over the last few years, the number of works about deep learning applied to the medical field has increased enormously. The necessity of a rigorous assessment of these models is required to explain these results to all people involved in medical exams. A recent field in the machine learning area is explainable artificial intelligence, also known as XAI, which targets to explain the results of such black box models to permit the desired assessment. This survey analyses several recent studies in the XAI field applied to medical diagnosis research, allowing some explainability of the machine learning results in several different diseases, such as cancers and COVID-19.