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Fostering responsible artificial intelligence: an evaluation approach grounded in counterfactual reasoning
Full text linkL'applicazione crescente di modelli di Intelligenza Artificiale e Machine Learning pone potenziali rischi di comportamenti non etici e, alla luce delle recenti normative, ha attirato l'attenzione della comunità di ricerca. Le attuali normative sull'IA richiedono di escludere caratteristiche sensibili (ad esempio, genere, razza, religione) dal processo decisionale degli algoritmi per prevenire risultati iniqui. Tuttavia, anche senza caratteristiche sensibili nel set di addestramento, gli algoritmi possono continuare a discriminare. Infatti, quando le caratteristiche sensibili sono omesse (equità sotto inconsapevolezza), queste potrebbero essere dedotte attraverso relazioni non lineari con le cosiddette caratteristiche proxy. Numerosi ricercatori si sono concentrati sulla definizione di nuove nozioni di equità o sullo sviluppo di approcci per identificare previsioni distorte, senza tuttavia affrontare la seguente domanda: quale definizione di equità dovrebbe essere adottata e soddisfatta in un modello implementato? Di conseguenza, quale metrica dovremmo utilizzare? E quali metriche possono meglio quantificare il comportamento iniquo di un modello? Queste domande rimangono sfide aperte nel campo. Inoltre, una limitazione degli approcci proposti è che si concentrano esclusivamente su uno spazio discreto e limitato; solo pochi analizzano le variazioni minime richieste nelle caratteristiche degli utenti per garantire un risultato positivo per gli individui (controfattuali).
Questa dissertazione mira a colmare il divario nel dominio dell'equità proponendo una nuova prospettiva sull'equità. Partendo dalla recente letteratura accademica nell'area, questa tesi si intreccerà con questioni vicine al campo dell'IA responsabile offrendo spunti nelle seguenti direzioni: (i) proponiamo un framework basato sul ragionamento controfattuale per rivelare il potenziale bias nascosto di un modello di machine learning che può persistere anche quando le caratteristiche sensibili sono escluse, (ii) proponiamo una procedura semplice per identificare e quantificare la relazione tra caratteristiche sensibili e caratteristiche proxy, (iii) utilizziamo il ragionamento controfattuale per spiegare le decisioni del modello costruendo una pipeline responsabile per il dominio del credito.The increasing application of Artificial Intelligence and Machine Learning models
poses potential risks of unethical behaviour and, in light of recent regulations, has
attracted the attention of the research community. Current AI regulations require
discarding sensitive features (e.g., gender, race, religion) in the algorithm’s decision-
making process to prevent unfair outcomes. However, even without sensitive features
in the training set, algorithms can persist in discrimination. Indeed, when sensitive
features are omitted (fairness under unawareness), they could be inferred through
non-linear relations with the so-called proxy features. Several researchers focused on
seeking new fairness definitions or developing approaches to identify biased predictions
without helping to answer the following question: Which fairness definition should
be used and satisfied in a deployed model? Consequently, what metric should we
satisfy? However, what metrics can better quantify the unfair behavior of a model?
These questions remain open challenges in the field. Furthermore, a limitation of the
proposed approaches is that they focus solely on a discrete and limited space; only a
few analyze the minimum variations required in the user characteristics to ensure a
positive outcome for the individuals (counterfactuals).
This dissertation aims to bridge the gap in the fairness domain by proposing a new
fairness perspective. Starting from the recent academic literature in the area, this thesis
will intertwine with the issues close to the field of responsible AI by offering insights
in the following directions: (i) we propose a framework grounded in counterfactual
reasoning to reveal the potential hidden bias of a machine learning model that can
persist even when sensitive features are discarded, (ii) we propose a simple procedure
to identify and quantify the relationship between sensitive characteristics and proxy
features. (iii) we leverage counterfactual reasoning to explain the model decision
building a responsible pipeline for the credit score domain
Modellamento trasversale di una spiaggia durante una mareggiata simulata con onde regolari ed irregolari - Indagine sperimentale
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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