An evaluation of the responsibility of generative AI models presents unique challenges that require holistic and practical solutions. This paper introduces an enhanced version of the VERIFAI framework, which extends beyond classification models to assess generative language models as well… Read More »Towards Responsibility Evaluation of Generative Language Models
In real-world data integration scenarios, traditional equi-joins and other join techniques have huge difficulties due to heterogenity and inconsistencies in attribute values. To address this challenge, we present AutoStarJoin, a technique for automated joins specifically designed for star-join scenarios. The core… Read More »Automating Data Fusion: Techniques for Handling of Join Scenarios
This paper examines the use of Large Language Models (LLMs) for the intralingual translation of documents from standard German to German Easy Language (Leichte Sprache). We use open-weight models, from the Llama 3 family, with less than ten billion parameters.… Read More »LLMs for Easy Language Translation: A Case Study on German Public Authorities Web Pages
This work represents the first step towards a unified framework for evaluating an AI system’s responsibility by building a prototype application.The python based web-application uses several libraries for testing the fairness, robustness, privacy, and explainability of a machine-learning model as… Read More »VERIFAI – A Step Towards Evaluating the Responsibility of AI-Systems
This paper presents a reinforcement learning approach for foreign exchange trading. Inspired by technical analysis methods, this approach makes use of technical indicators by encoding them into Gramian Angular Fields and searches for patterns that indicate price movements using convolutional… Read More »Scalp the Foreign Exchange Market with Deep Reinforcement Learning
Detecting erroneous or fraudulent business transactions and corre-sponding journal entries imposes a significant challenge for auditors during annualaudits. One possible solution to cope with these problems is the use of machinelearning methods, such as an autoencoder, to identify unusual journal… Read More »XAI in the Audit Domain – Explaining an Autoencoder Model for Anomaly Detection
This work introduces Multi-Fusion Network for human-object interaction detection with multiple cameras. We present a concept and implementation of the architecture for a beverage refrigerator with multiple cameras as proof-of-concept. We also introduce an effective approach for minimizing the required… Read More »Recognizing Human-Object Interaction in Multi-Camera Environments