Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown

An AI-based Framework For Parent-child Interaction Analysis

The quality of parent-child interactions is foundational to children's social-emotional and cognitive development, as well as their lifelong mental health. The Parent-Child Interaction Teaching Scale (PCITS) is a well-established and effective tool used to measure parent-child interaction quality. It is utilized in both public health settings and basic and applied research studies to identify problem areas within parent-child interactions. However, like other observational measures of parent-child interaction quality, the PCITS can be time-consuming to administer and score, which limits its wider implementation. Therefore, the main objective of this research is to organize a framework for the recognition of behavioural symptoms of the child and parent during interventions. Based on the literature on interactive parent-child behaviour analysis, we categorized PCITS labels into three modalities: language, audio, and video. Some labels have dyadic actors, while others have a single actor (either the parent or child). In addition, within each modality, there are technical issues, considerations, and limitations in terms of artificial intelligence. Hence, we divided the problem into three modalities, proposed models for each modality, and a solution to combine them. Firstly, we proposed a model for recognizing action-related labels (video). These labels are interactive and involve two actors: the parent and the child. We conducted a feature extraction algorithm to produce semantic features passed through a feature selection algorithm to extract the most meaningful semantic features from the video. We chose this method due to its lower data requirement compared to other modalities. Also, because of using 2D video files, the proposed feature extraction and selection algorithms are to handle the occlusion and natural conditions like camera movement, Secondly, we proposed a model for recognizing language- and audio-related labels. These labels represent a single-actor role for the parent, as children are not yet capable of producing meaningful text in the intervention videos. To develop this model, we conducted research on a similar dataset to utilize transfer learning between two problems. Therefore, the second part of this research is associated with working on this text dataset. Third, we focused on multi-modal aspects of the work. We conducted experiments to determine how to integrate the prior work into our model. We also provided an ensemble model, which combined the modalities of language and audio based on the semantic and syntactic characteristics of the text. This ensemble model provides a baseline for developing further models with different aspects and modalities. Finally, we provided a roadmap to support more labels that were not covered in this research due to not reaching enough samples. Our proposed framework includes a labelling system that we developed in the primary stages of the research to gather labelled data. This system also plays a role to be integrated with AI modules to provide auto-recognition of the behavioural labels in parent-child interaction videos to the nurses

The Psychology of Fake News

This volume examines the phenomenon of fake news by bringing together leading experts from different fields within psychology and related areas, and explores what has become a prominent feature of public discourse since the first Brexit referendum and the 2016 US election campaign. Dealing with misinformation is important in many areas of daily life, including politics, the marketplace, health communication, journalism, education, and science. In a general climate where facts and misinformation blur, and are intentionally blurred, this book asks what determines whether people accept and share (mis)information, and what can be done to counter misinformation? All three of these aspects need to be understood in the context of online social networks, which have fundamentally changed the way information is produced, consumed, and transmitted. The contributions within this volume summarize the most up-to-date empirical findings, theories, and applications and discuss cutting-edge ideas and future directions of interventions to counter fake news. Also providing guidance on how to handle misinformation in an age of “alternative facts”, this is a fascinating and vital reading for students and academics in psychology, communication, and political science and for professionals including policy makers and journalists

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)