53 research outputs found

    Effects of Logic-Style Explanations and Uncertainty on Users’ Decisions

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    The spread of innovative Artificial Intelligence (AI) algorithms assists many individuals in their daily life decision-making tasks but also sensitive domains such as disease diagnosis and credit risk. However, a great majority of these algorithms are of a black-box nature, bringing the need to make them more transparent and interpretable along with the establishment of guidelines to help users manage these systems. The eXplainable Artificial Intelligence (XAI) community investigated numerous factors influencing subjective and objective metrics in the user-AI team, such as the effects of presenting AI-related information and explanations to users. Nevertheless, some factors that influence the effectiveness of explanations are still under-explored in the literature, such as user uncertainty, AI uncertainty, AI correctness, and different explanation styles. The main goal of this thesis is to investigate the interactions between different aspects of decision-making, focusing in particular on the effects of AI and user uncertainty, AI correctness, and the explanation reasoning style (inductive, abductive, and deductive) on different data types and domains considering classification tasks. We set up three user evaluations on images, text, and time series data to analyse these factors on users' task performance, agreement with the AI suggestion, and the user’s reliance on the XAI interface elements (instance, AI prediction, and explanation). The results for the image and text data show that user uncertainty and AI correctness on predictions significantly affected users’ classification decisions considering the analysed metrics. In both domains (images and text), users relied mainly on the instance to decide. Users were usually overconfident about their choices, and this evidence was more pronounced for text. Furthermore, the inductive style explanations led to over-reliance on AI advice in both domains – it was the most persuasive, even when the AI was incorrect. The abductive and deductive styles have complex effects depending on the domain and the AI uncertainty levels. Instead, the time series data results show that specific explanation styles (abductive and deductive) improve the user’s task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user’s decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. The last part of the thesis focuses on the work done with the \enquote{CRS4 - Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna}, for the implementation of the RIALE (Remote Intelligent Access to Lab Experiment) Platform. The work aims to help students explore a DNA-sequences experiment enriched with an AI tagging tool, which detects the objects used in the laboratory and its current phase. Further, the interface includes an interactive timeline which enables students to explore the AI predictions of the video experiment's steps and an XAI panel that provides explanations of the AI decisions - presented with abductive reasoning - on three levels (globally, by phase, and by frame). We evaluated the interface with students considering the subjective cognitive effort, ease of use, supporting information of the interface, general usability, and an interview on a set of questions on peculiar aspects of the application. The user evaluation results showed that students were positively satisfied with the interface and in favour of following didactic lessons using this tool

    Constitutional Challenges in the Algorithmic Society

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    The law struggles to address the constitutional challenges of the algorithmic society. This book is for scholars and lawyers interested in the intersections of law and technology. It addresses the challenges for fundamental rights and democracy, the role of policy and regulation, and the responsibilities of private actors

    Constitutional challenges in the algorithmic society

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    From the publisher: This work is in copyright. It is subject to statutory exceptions and to the provisions of relevant licensing agreements; with the exception of the Creative Commons version the link for which is provided below, no reproduction of any part of this work may take place without the written permission of Cambridge University Press. An online version of this work is published at doi.org/10.1017/9781108914857 under a Creative Commons Open Access license CC-BY-NC-ND 4.0 which permits re-use, distribution and reproduction in any medium for non-commercial purposes providing appropriate credit to the original work is given. You may not distribute derivative works without permission. To view a copy of this license, visit https:// creativecommons.org/licenses/by-nc-nd/4.0New technologies have always challenged the social, economic, legal, and ideological status quo. Constitutional law is no less impacted by such technologically driven transformations, as the state must formulate a legal response to new technologies and their market applications, as well as the state's own use of new technology. In particular, the development of data collection, data mining, and algorithmic analysis by public and private actors present unique challenges to public law at the doctrinal as well as the theoretical level. This collection, aimed at legal scholars and practitioners, describes the constitutional challenges created by the algorithmic society. It offers an important synthesis of the state of play in law and technology studies, addressing the challenges for fundamental rights and democracy, the role of policy and regulation, and the responsibilities of private actors. This title is also available as Open Access on Cambridge Core.-- Oreste Pollicino and Giovanni De Gregorio, Constitutional law in the algorithmic society -- Andrea Simoncini and Erik Longo, Fundamental rights and the rule of law in the algorithmic society by law? -- Celine Castest-Renard, Human rights and algorithmic impact assessment for predictive policing -- Francesca Galli, Law enforcement and data-driven predictions at the national and EU level : a challenge to the presumption of innocence and reasonable suspicion? -- Amnon Reichman and Giovanni Sartor, Algorithms and regulation -- Angela Daly, Thilo Hagendorff, Li Hui, Monique Mann, Vidushi Marda, Ben Wagner, Wayne Wei Wang, Artificial Intelligence, governance and ethics : global perspectives -- Pieter Vancleynenbreugel, EU by-design regulation in the algorithmic society : promising way forward or constitutional nightmare in-the-making? -- Henrik Palmer Olsen, Jacob Livingston Slosser and Thomas Troels Hildebrandt, What's in the box? The legal requirement of explainability in computationally, eided decision-making in public ddministration -- Yaiza Cabedo, The international regulatory race for protecting investors from crypto-finance risks -- Hans W. Micklitz and Anne Aurelie Villanueva, Responsibilities of companies in the algorithmic society -- Serge Gijrath, Consumer law as a tool to regulate adverse consequences of AI output -- Federica Casarosa, When the algorithm is not fully reliable : the collaboration between technology and humans in the fight against hate speech -- Pietro Sirena and Francesco Paolo Patti, Smart contracts and automation of private relationship

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

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    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)

    Semantics-driven Abstractive Document Summarization

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    The evolution of the Web over the last three decades has led to a deluge of scientific and news articles on the Internet. Harnessing these publications in different fields of study is critical to effective end user information consumption. Similarly, in the domain of healthcare, one of the key challenges with the adoption of Electronic Health Records (EHRs) for clinical practice has been the tremendous amount of clinical notes generated that can be summarized without which clinical decision making and communication will be inefficient and costly. In spite of the rapid advances in information retrieval and deep learning techniques towards abstractive document summarization, the results of these efforts continue to resemble extractive summaries, achieving promising results predominantly on lexical metrics but performing poorly on semantic metrics. Thus, abstractive summarization that is driven by intrinsic and extrinsic semantics of documents is not adequately explored. Resources that can be used for generating semantics-driven abstractive summaries include: • Abstracts of multiple scientific articles published in a given technical field of study to generate an abstractive summary for topically-related abstracts within the field, thus reducing the load of having to read semantically duplicate abstracts on a given topic. • Citation contexts from different authoritative papers citing a reference paper can be used to generate utility-oriented abstractive summary for a scientific article. • Biomedical articles and the named entities characterizing the biomedical articles along with background knowledge bases to generate entity and fact-aware abstractive summaries. • Clinical notes of patients and clinical knowledge bases for abstractive clinical text summarization using knowledge-driven multi-objective optimization. In this dissertation, we develop semantics-driven abstractive models based on intra- document and inter-document semantic analyses along with facts of named entities retrieved from domain-specific knowledge bases to produce summaries. Concretely, we propose a sequence of frameworks leveraging semantics at various granularity (e.g., word, sentence, document, topic, citations, and named entities) levels, by utilizing external resources. The proposed frameworks have been applied to a range of tasks including 1. Abstractive summarization of topic-centric multi-document scientific articles and news articles. 2. Abstractive summarization of scientific articles using crowd-sourced citation contexts. 3. Abstractive summarization of biomedical articles clustered based on entity-relatedness. 4. Abstractive summarization of clinical notes of patients with heart failure and Chest X-Rays recordings. The proposed approaches achieve impressive performance in terms of preserving semantics in abstractive summarization while paraphrasing. For summarization of topic-centric multiple scientific/news articles, we propose a three-stage approach where abstracts of scientific articles or news articles are clustered based on their topical similarity determined from topics generated using Latent Dirichlet Allocation (LDA), followed by extractive phase and abstractive phase. Then, in the next stage, we focus on abstractive summarization of biomedical literature where we leverage named entities in biomedical articles to 1) cluster related articles; and 2) leverage the named entities towards guiding abstractive summarization. Finally, in the last stage, we turn to external resources such as citation contexts pointing to a scientific article to generate a comprehensive and utility-centric abstractive summary of a scientific article, domain-specific knowledge bases to fill gaps in information about entities in a biomedical article to summarize and clinical notes to guide abstractive summarization of clinical text. Thus, the bottom-up progression of exploring semantics towards abstractive summarization in this dissertation starts with (i) Semantic Analysis of Latent Topics; builds on (ii) Internal and External Knowledge-I (gleaned from abstracts and Citation Contexts); and extends it to make it comprehensive using (iii) Internal and External Knowledge-II (Named Entities and Knowledge Bases)

    Urban Planet

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    Global urbanization promises better services and stronger economies but also carries risks and unforeseeable consequences. Urban Planet highlights the hopes and hindrances of our journey of urbanization and the need for a parallel evolution of our science and systems to ensure we reap the rewards. This title is also available as Open Access

    Argumentation, Ideology and Discourse in Evolving Specialized Communication

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    In the digital age, the transformation process of information into ‘knowledge’ is characterized by hyper-connected communities, where a potentially infinite amount of information is ubiquitously accessible to individuals or community users and is instrumental in the creation of shared knowledge, but also in building consensus across community participants, societal membership and grouping, through the argumentative ideological representation of assumptions, values and practices. This Special Issue of “Lingue e Linguaggi” on the theme Argumentation, Ideology and Discourse in Evolving Specialized Communication explores the interface between these three dimensions and combines an array of perspectives into a distinctly unified volume, offering synchronic, diachronic, comparative, interlinguistic and intercultural approaches over a range of specialized knowledge domains. The volume integrates quantitative and qualitative approaches, making use of Corpus Linguistics, alongside other methods incorporated in theoretical approaches such as Critical Discourse Analysis, Appraisal Theory and Argumentation Theory, focusing on the pragma-linguistic features of different texts and genres, together with their ideological purposes for different audiences in various contexts of use. The collection of essays investigates argumentative styles and patterning along with the discursive socio-construction of ideology in the dynamics of recontextualization, rescripting and remediation which affect the multi-faceted nature of contemporary communication
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