As artificial intelligence (AI) systems become increasingly integrated into everyday life, the need for trustworthiness in these systems has emerged as a critical challenge.This tutorial paper addresses the complexity of building trust in AI systems by exploring recent advances in explainable AI (XAI) and related areas that go beyond mere interpretability.After reviewing recent trends in XAI, we discuss how to control AI systems, align them with societal concerns, and address the robustness, reproducibility, and evaluation concerns inherent in these systems.This review highlights the multifaceted nature of the mechanisms for building trust in AI, and we hope it will pave the way for further research in this area.1 https://digital-strategy.

Recent technological advances rely on accurate decision support systems that can be perceived as black boxes due to their overwhelming complexity. This lack of transparency can lead to technical, ethical, legal, and trust issues. For example, if the control module of a self-driving car failed at detecting a pedestrian, it becomes crucial to know why the system erred. In some other cases, the decision system may reflect unacceptable biases that can generate distrust. The General Data Protection Regulation (GDPR), approved by the European Parliament in 2018, suggests that individuals should be able to obtain explanations of the decisions made from their data by automated processing, and to challenge those decisions. All these reasons have given rise to the domain of interpretable and explainable AI. AIMLAI aims at gathering researchers, experts and professionals, from inside and outside the domain of AI, interested in the topic of interpretable ML and interpretable AI. The workshop encourages interdisciplinary collaborations, with particular emphasis in knowledge management, Infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters. AIMLAI envisions to become a discussion venue for the advent of novel interpretable algorithms and explainability modules that mediate the communication between complex ML/AI systems and users.

The Third Workshop on "Advances in Interpretable Machine Learning and Artificial Intelligence" (AIMLAI) presents contributions in the fields of (i) interpretable ML and AI, i.e., algorithms that are natively interpretable, and (ii) interpretability modules, i.e., explanation layers on top of black-box models, also called post-hoc interpretability. AIMLAI encourages interdisciplinary collaborations with particular emphasis in knowledge management, infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters.

Facing an unknown situation, a person may not be able to firmly elicit his/her preferences over different alternatives, so he/she tends to express uncertain preferences. Given a community of different persons expressing their preferences over certain alternatives under uncertainty, to get a collective representative opinion of the whole community, a preference fusion process is required. The aim of this work is to propose a preference fusion method that copes with uncertainty and escape from the Condorcet paradox. To model preferences under uncertainty, we propose to develop a model of preferences based on belief function theory that accurately describes and captures the uncertainty associated with individual or collective preferences. This work improves and extends the previous results. This work improves and extends the contribution presented in a previous work. The benefits of our contribution are twofold. On the one hand, we propose a qualitative and expressive preference modeling strategy based on belief-function theory which scales better with the number of sources. On the other hand, we propose an incremental distance-based algorithm (using Jousselme distance) for the construction of the collective preference order to avoid the Condorcet Paradox.