activity
20232026
most citedData-Driven Analysis of Gender Fairness in the Software Engineering Academic Landscape

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.SE2026

SafeTune: Search-based Harmfulness Minimisation for Large Language Models

Giordano d'Aloisio, David Williams, Giusy Annunziata +3

The widespread adoption of Large Language Models (LLMs) raises concerns about the potential harmfulness of their responses. In this paper, we first investigate the harmfulness of r…

cs.LG2026

A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks

Maryam Boubekraoui, Giordano d'Aloisio, Antinisca Di Marco

The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness. While the research community has introduced various methods for bias mitigatio…

cs.SE20241 cited

VAMP: Visual Analytics for Microservices Performance

Luca Traini, Jessica Leone, Giovanni Stilo +1

Analysis of microservices' performance is a considerably challenging task due to the multifaceted nature of these systems. Each request to a microservices system might raise severa…

cs.SE2024

How fair are we? From conceptualization to automated assessment of fairness definitions

Giordano d'Aloisio, Claudio Di Sipio, Antinisca Di Marco +1

Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems. With the increasing use of machine learning in s…

cs.CY20231 cited

Data-Driven Analysis of Gender Fairness in the Software Engineering Academic Landscape

Giordano d'Aloisio, Andrea D'Angelo, Francesca Marzi +3

Gender bias in education gained considerable relevance in the literature over the years. However, while the problem of gender bias in education has been widely addressed from a stu…

cs.LG2023

Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development

Francesca Marzi, Giordano d'Aloisio, Antinisca Di Marco +1

The problem of predicting the training time of machine learning (ML) models has become extremely relevant in the scientific community. Being able to predict a priori the training t…