activity
20242026
most citedData Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?

1 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SE2026

Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models

Gianmario Voria, Moses Openja, Foutse Khomh +2

The advent of transformer-based language models has reshaped how AI systems process and generate text. In software engineering (SE), these models now support diverse activities, ac…

cs.SE2026

Once Upon a Team: Investigating Bias in LLM-Driven Software Team Composition and Task Allocation

Alessandra Parziale, Gianmario Voria, Valeria Pontillo +4

LLMs are increasingly used to boost productivity and support software engineering tasks. However, when applied to socially sensitive decisions such as team composition and task all…

cs.SE2025

Toward Systematic Counterfactual Fairness Evaluation of Large Language Models: The CAFFE Framework

Alessandra Parziale, Gianmario Voria, Valeria Pontillo +3

Nowadays, Large Language Models (LLMs) are foundational components of modern software systems. As their influence grows, concerns about fairness have become increasingly pressing.…

cs.SE2025

Contextual Fairness-Aware Practices in ML: A Cost-Effective Empirical Evaluation

Alessandra Parziale, Gianmario Voria, Giammaria Giordano +3

As machine learning (ML) systems become central to critical decision-making, concerns over fairness and potential biases have increased. To address this, the software engineering (…

cs.SE20241 cited

Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?

Gianmario Voria, Rebecca Di Matteo, Giammaria Giordano +2

As machine learning (ML) systems are increasingly adopted across industries, addressing fairness and bias has become essential. While many solutions focus on ethical challenges in…

cs.SE20241 cited

From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?

Gianmario Voria, Stefano Lambiase, Maria Concetta Schiavone +2

As the adoption of machine learning (ML) systems continues to grow across industries, concerns about fairness and bias in these systems have taken center stage. Fairness toolkits,…