Unfairness Assessment, Explanation and Mitigation in Machine Learning Models for Personalization
type: CreativeWork
description: The last decade has been pervaded by the automatic applications leveraging Artificial Intelligence technologies. Novel systems have been adopted to automatically solve relevant tasks, from scanning passengers during border controls to suggesting the groceries to buy to fill the fridge. One of the most captivating applications of Artificial Intelligence is represented by voice assistants, like Alexa. They enable people to use their voice to perform simple tasks, such as setting an alarm or saving an appointment in an online calendar. Due to their worldwide usage, voice assistants are required to aid a diverse range of individuals encompassing various cultures, languages, accents, and preferences. It is then crucial for these systems to function fairly across different groups of people to ensure reliability and provide assistance without being influenced by sensitive attributes that may vary among them. This thesis deals with the design, implementation, and evaluation of Artificial Intelligence models that are optimized to operate fairly in the context of voice assistant systems. Assessing the level of performance of existing fairness-aware solutions is an essential step towards comprehending how much effort should be put to provide fair and reliable technologies. The contributions result in extensive analyses of existing methods to counteract unfairness, and in novel techniques to mitigate and explain unfairness that capitalize on Data Balancing, Counterfactuality, and Graph Neural Networks Explainability. The proposed solutions aim to support system designers and decision makers over several fairness requirements. Specifically, over methodologies to evaluate fairness of models outcomes, techniques aimed to improve users’ trustworthiness by mitigating unfairness, and strategies that generate explanations of the potential causes behind the estimated unfairness. Through our studies, we explore opportunities and challenges introduced by the latest advancements in Fair A…
abstract: The last decade has been pervaded by the automatic applications leveraging Artificial Intelligence technologies. Novel systems have been adopted to automatically solve relevant tasks, from scanning passengers during border controls to suggesting the groceries to buy to fill the fridge. One of the most captivating applications of Artificial Intelligence is represented by voice assistants, like Alexa. They enable people to use their voice to perform simple tasks, such as setting an alarm or saving an appointment in an online calendar. Due to their worldwide usage, voice assistants are required to aid a diverse range of individuals encompassing various cultures, languages, accents, and preferences. It is then crucial for these systems to function fairly across different groups of people to ensure reliability and provide assistance without being influenced by sensitive attributes that may vary among them. This thesis deals with the design, implementation, and evaluation of Artificial Intelligence models that are optimized to operate fairly in the context of voice assistant systems. Assessing the level of performance of existing fairness-aware solutions is an essential step towards comprehending how much effort should be put to provide fair and reliable technologies. The contributions result in extensive analyses of existing methods to counteract unfairness, and in novel techniques to mitigate and explain unfairness that capitalize on Data Balancing, Counterfactuality, and Graph Neural Networks Explainability. The proposed solutions aim to support system designers and decision makers over several fairness requirements. Specifically, over methodologies to evaluate fairness of models outcomes, techniques aimed to improve users’ trustworthiness by mitigating unfairness, and strategies that generate explanations of the potential causes behind the estimated unfairness. Through our studies, we explore opportunities and challenges introduced by the latest advancements in Fair A…
author: MEDDA, GIACOMO
language: ita
keywords: Settore INF/01 - Informatica
date published: 2024-02-20T00:00:00+01:00
date modified: 2026-07-24T01:29:57Z
provider: Università di Cagliari
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Università degli Studi di Cagliari

PropertyValue
property ID: Handle
value: https://hdl.handle.net/11584/391987