Explainable Deep Learning

Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelligence o XAI, uno degli ambiti di ricerca che vanno per la maggiore al giorno d'oggi in ambito di Deep Learning e di Intelligenza Artificiale. Il concetto alla base di questo filone di ricerca è quello di rendere e/o progettare i nuovi algoritmi di Deep Learning in modo che siano affidabili, interpretabili e comprensibili all'uomo. Questa necessità è dovuta proprio al fatto che le reti neurali, modello matematico che sta alla base del Deep Learning, agiscono come una scatola nera, rendendo incomprensibile all'uomo il ragionamento interno che compiono per giungere ad una decisione. Dato che stiamo delegando a questi modelli matematici decisioni sempre più importanti, integrandole nei processi più delicati della nostra società quali, ad esempio, la diagnosi medica, la guida autonoma o i processi di legge, è molto importante riuscire a comprendere le motivazioni che portano questi modelli a produrre determinati risultati. Il lavoro presentato in questa tesi consiste proprio nello studio e nella sperimentazione di algoritmi di Deep Learning integrati con tecniche di Intelligenza Artificiale simbolica. Questa integrazione ha un duplice scopo: rendere i modelli più potenti, consentendogli di compiere ragionamenti o vincolandone il comportamento in situazioni complesse, e renderli interpretabili. La tesi affronta due macro argomenti: le spiegazioni ottenute grazie all'integrazione neuro-simbolica e lo sfruttamento delle spiegazione per rendere gli algoritmi di Deep Learning più capaci o intelligenti. Il primo macro argomento si concentra maggiormente sui lavori svolti nello sperimentare l'integrazione di algoritmi simbolici con le reti neurali. Un approccio è stato quelli di creare un sistema per guidare gli addestramenti delle reti stesse in modo da trovare la migliore combinazione di iper-parametri per automatizzare la progettazione stessa di queste reti. Questo è fatto tramite l'integrazione di reti neurali con la Programmazione Logica Probabilistica (PLP) che consente di sfruttare delle regole probabilistiche indotte dal comportamento delle reti durante la fase di addestramento o ereditate dall'esperienza maturata dagli esperti del settore. Queste regole si innescano allo scatenarsi di un problema che il sistema rileva durate l'addestramento della rete. Questo ci consente di ottenere una spiegazione di cosa è stato fatto per migliorare l'addestramento una volta identificato un determinato problema. Un secondo approccio è stato quello di far cooperare sistemi logico-probabilistici con reti neurali per la diagnosi medica da fonti di dati eterogenee. La seconda tematica affrontata in questa tesi tratta lo sfruttamento delle spiegazioni che possiamo ottenere dalle rete neurali. In particolare, queste spiegazioni sono usate per creare moduli di attenzione che aiutano a vincolare o a guidare le reti neurali portandone ad avere prestazioni migliorate. Tutti i lavori sviluppati durante il dottorato e descritti in questa tesi hanno portato alle pubblicazioni elencate nel Capitolo 14.2.The great success that Machine and Deep Learning has achieved in areas that are strategic for our society such as industry, defence, medicine, etc., has led more and more realities to invest and explore the use of this technology. Machine Learning and Deep Learning algorithms and learned models can now be found in almost every area of our lives. From phones to smart home appliances, to the cars we drive. So it can be said that this pervasive technology is now in touch with our lives, and therefore we have to deal with it. This is why eXplainable Artificial Intelligence or XAI was born, one of the research trends that are currently in vogue in the field of Deep Learning and Artificial Intelligence. The idea behind this line of research is to make and/or design the new Deep Learning algorithms so that they are interpretable and comprehensible to humans. This necessity is due precisely to the fact that neural networks, the mathematical model underlying Deep Learning, act like a black box, making the internal reasoning they carry out to reach a decision incomprehensible and untrustable to humans. As we are delegating more and more important decisions to these mathematical models, it is very important to be able to understand the motivations that lead these models to make certain decisions. This is because we have integrated them into the most delicate processes of our society, such as medical diagnosis, autonomous driving or legal processes. The work presented in this thesis consists in studying and testing Deep Learning algorithms integrated with symbolic Artificial Intelligence techniques. This integration has a twofold purpose: to make the models more powerful, enabling them to carry out reasoning or constraining their behaviour in complex situations, and to make them interpretable. The thesis focuses on two macro topics: the explanations obtained through neuro-symbolic integration and the exploitation of explanations to make the Deep Learning algorithms more capable or intelligent. The neuro-symbolic integration was addressed twice, by experimenting with the integration of symbolic algorithms with neural networks. A first approach was to create a system to guide the training of the networks themselves in order to find the best combination of hyper-parameters to automate the design of these networks. This is done by integrating neural networks with Probabilistic Logic Programming (PLP). This integration makes it possible to exploit probabilistic rules tuned by the behaviour of the networks during the training phase or inherited from the experience of experts in the field. These rules are triggered when a problem occurs during network training. This generates an explanation of what was done to improve the training once a particular issue was identified. A second approach was to make probabilistic logic systems cooperate with neural networks for medical diagnosis on heterogeneous data sources. The second topic addressed in this thesis concerns the exploitation of explanations. In particular, the explanations one can obtain from neural networks are used in order to create attention modules that help in constraining and improving the performance of neural networks. All works developed during the PhD and described in this thesis have led to the publications listed in Chapter 14.2

Does fake news affect voting behaviour?

We study the impact of fake news on votes for populist parties in the Italian elections of 2018. Our empirical strategy exploits the presence of Italian- and German-speaking voters in the Italian region of Trentino Alto-Adige/Südtirol as an exogenous source of assignment to fake news exposure. Using municipal data, we compare the effect of exposure to fake news on the vote for populist parties in the 2013 and 2018 elections. To do so, we introduce a novel indicator of populism using text mining on the Facebook posts of Italian parties before the elections. We find that exposure to fake news is positively correlated with vote for populist parties, but that less than half of this correlation is causal. Our findings support the view that exposure to fake news (i) favours populist parties, but also that (ii) it is positively correlated with prior support for populist parties, suggesting a self-selection mechanism

Exploiting CNN’s visual explanations to drive anomaly detection

Nowadays, deep learning is a key technology for many applications in the industrial area such as anomaly detection. The role of Machine Learning (ML) in this field relies on the ability of training a network to learn to inspect images to determine the presence or not of anomalies. Frequently, in Industry 4.0 w.r.t. the anomaly detection task, the images to be analyzed are not optimal, since they contain edges or areas, that are not of interest which could lead the network astray. Thus, this study aims at identifying a systematic way to train a neural network to make it able to focus only on the area of interest. The study is based on the definition of a loss to be applied in the training phase of the network that, using masks, gives higher weight to the anomalies identified within the area of interest. The idea is to add an Overlap Coefficient to the standard cross-entropy. In this way, the more the identified anomaly is outside the Area of Interest (AOI) the greater is the loss. We call the resulting loss Cross-Entropy Overlap Distance (CEOD). The advantage of adding the masks in the training phase is that the network is forced to learn and recognize defects only in the area circumscribed by the mask. The added benefit is that, during inference, these masks will no longer be needed. Therefore, there is no difference, in terms of execution times, between a standard Convolutional Neural Network (CNN) and a network trained with this loss. In some applications, the masks themselves are determined at run-time through a trained segmentation network, as we have done for instance in the "Machine learning for visual inspection and quality control" project, funded by the MISE Competence Center Bi-REX

Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients

Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

Virtual prototyping of smart systems through automatic abstraction and mixed-signal scheduling

Modern smart systems are usually built by implementing SW functionalities executed on HW platforms composed of both digital and analog components. Validation is mainly implemented through simulation of the functional behavior of the entire smart system modeled by a Virtual Platform. It is thus crucial to achieve fast mixed-signal simulation by removing unnecessary overhead due to synchronization between multiple tools and unimportant details. This work proposes a methodology to abstract mixed-signal systems, by integrating digital and analog components in a homogeneous virtual platform model for efficient simulation. Two main contributions are provided: 1) an automatic abstraction technique for analog components, allowing to preserve only the details meaningful for the functional behavior of the entire platform by moving complexity from simulation to generation time and 2) a novel scheduling technique that exploits temporal decoupling and synchronization of digital and analog processes, to simulate them together in a homogeneous model

Automatic abstraction of multi-discipline analog models for efficient functional simulation

Multi-discipline components introduce problems when inserted within virtual platforms of Smart Systems for functional validation. This paper lists the most common emerging problems and it proposes a set of solutions to them. It presents a set of techniques, unified in an automatic abstraction methodology, useful to achieve fast analog mixed-signal simulation even when different physical disciplines and modeling styles are combined into a single analog model. The paper makes use of a complex case study. It deals with multiple-discipline descriptions, non-electrical conservative models, non-linear equation systems, and mixed time/frequency domain models. The original component behavior has been modeled in Verilog-AMS by using electrical, mechanical and kinematic equations. Then, it has been abstracted and integrated within a virtual platform of a mixed-signal smart system for efficient functional simulation

Automatic Generation of Analog/Mixed Signal Virtual Platforms for Smart Systems

Pervasive computing requires to build systems every day more complex and heterogeneous. Smart devices must be able to carry on sensing and actuation alongside with computation and communication. As such, many different technologies must be packed within the same object. Digital HW and SW coexist with analog components and Micro-Electro-Mechanical systems capable of sensing and controlling the physical environment. For this reason, the design of such devices must rely on the integration of many different descriptions belonging to different design domains. The high-level of heterogeneity involved in the modeling phase of the system development makes harder the validation of the system functionality, since holistic system simulation would require the integration of many different simulators. In this article, we propose a set of automatic abstraction techniques for multi-disciplines analog components. Then, we define a scheduling strategy to integrate the execution of continuous-time analog sub-components with automatically abstracted models of the digital HW parts of the system. As a final result, the proposed methodology produces a C++ virtual platform providing a holistic simulation of complex and heterogeneous devices

Machine learning techniques for extracting relevant features from clinical data for COVID-19 mortality prediction

The role of Machine Learning (ML) in healthcare is based on the ability of a machine to analyse the huge amounts of data available for each patient, like age, medical history, overall health status, test results, etc. With ML algorithms it is possible to learn models from data for the early identification of pathologies and their severity. Early identification is crucial to proceed as soon as possible with the necessary therapeutic actions. This work applies modern ML techniques to clinical data of either COVID-19 positive and COVID-19 negative patients with pulmonary complications, to learn mortality prediction models for both groups of patients, and compare results. We have focused on symbolic methods for building classifiers able to extract patterns from clinical data. This approach leads to predictive Artificial Intelligence (AI) systems working on medical data, and able to explain the reasons that lead the systems themselves to reach a certain conclusion