Meaningful insights: explainability techniques for black-box models on tabular data

Artificial Intelligence (AI) and Machine Learning (ML) are novel data analysis techniques providing very accurate prediction results. They are widely adopted in a variety of industries to improve efficiency and decision-making, but they are also being used to develop intelligent systems. Their success grounds upon complex mathematical models, whose decisions and rationale are usually difficult to comprehend for human users to the point of being dubbed as black-boxes. This is particularly relevant in sensitive and highly regulated domains. To mitigate and possibly solve this issue, the Explainable AI (XAI) field became prominent in recent years. XAI consists of models and techniques to enable understanding of the intricated patterns discovered by black-box models. In this thesis, we consider model-agnostic XAI techniques, which can be applied to Tabular data, with a particular focus on the Credit Scoring domain. Special attention is dedicated to the LIME framework, for which we propose several modifications to the vanilla algorithm, in particular: a pair of complementary Stability Indices that accurately measure LIME stability, and the OptiLIME policy which helps the practitioner finding the proper balance among explanations' stability and reliability. We subsequently put forward GLEAMS a model-agnostic surrogate interpretable model which requires to be trained only once, while providing both Local and Global explanations of the black-box model. GLEAMS produces feature attributions and what-if scenarios, from both dataset and model perspective. Eventually, we argue that synthetic data are an emerging trend in AI, being more and more used to train complex models instead of original data. To be able to explain the outcomes of such models, we must guarantee that synthetic data are reliable enough to be able to translate their explanations to real-world individuals. To this end we propose DAISYnt, a suite of tests to measure synthetic tabular data quality and privacy.

I beni relazionali nella ricerca sociologica: che cosa sono e quali effetti producono

This paper studies relational goods as immaterial assets creating real effects in society. The work starts answering to this question: what kind of effects do relational goods produce? After an accurate literature examination we suppose relational goods are social relations of second order. In the hypotesis they come from the emergence of two distinct social relations: interpersonal and reflexive relations. We describe empirical evidences of these emergent assets in social life and we test the effects they produce with a model. In the work we focus on four targets. First of all we describe the emergence of relational goods through a mathematical model. Then we individualize social realities where relational goods show evident effects and we outline our scientific hypotesis. The following step consists in the formulation of empirical tests. At last we explain final results. Our aim is to set apart the constitutive structure of relational goods into a checkable model coherently with the empirical evidences shown in the research. In the study we use multi-variate analysis techniques to see relational goods in a new way and we use qualitative and quantitative strategies. Relational goods are analysed both as dependent and independent variable in order to consider causative factors acting in a black-box model. Moreover we analyse effects of relational goods inside social spheres, especially in third sector and capitalistic economy. Finally we attain to effective indexes of relational goods in order to compare them with some performance indexes.