1 citations · 1 across the 5 of their papers we have counts for
6 papers
The Gold in Bias: Maturing the AI Design Process through Verification
Samira Maghool, Paolo Ceravolo
Bias in AI systems is typically framed as a flaw to be minimized, yet it also serves as a critical indicator of underlying weaknesses in data, modeling assumptions, and system desi…
Artificial Conversations, Real Results: Fostering Language Detection with Synthetic Data
Fatemeh Mohammadi, Tommaso Romano, Samira Maghool +1
Collecting high-quality training data is essential for fine-tuning Large Language Models (LLMs). However, acquiring such data is often costly and time-consuming, especially for non…
Identifying Gender Stereotypes and Biases in Automated Translation from English to Italian using Similarity Networks
Fatemeh Mohammadi, Marta Annamaria Tamborini, Paolo Ceravolo +2
This paper is a collaborative effort between Linguistics, Law, and Computer Science to evaluate stereotypes and biases in automated translation systems. We advocate gender-neutral…
Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes
Mohammad Derakhshan, Paolo Ceravolo, Fatemeh Mohammadi
This paper investigates the effectiveness of GPT-4o-2024-08-06, one of the Large Language Models (LLM) from OpenAI, in detecting business process anomalies, with a focus on rework…
Enhancing Model Fairness and Accuracy with Similarity Networks: A Methodological Approach
Samira Maghool, Paolo Ceravolo
In this paper, we propose an innovative approach to thoroughly explore dataset features that introduce bias in downstream machine-learning tasks. Depending on the data format, we u…
Are Large Language Models the New Interface for Data Pipelines?
Sylvio Barbon Junior, Paolo Ceravolo, Sven Groppe +5
A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant at…