1,187 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    The Radical Right in England and Wales: Permission to Hate?

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    Despite the prominence of the radical right in the UK, scant research has been undertaken to explore the influence that these organisations have on the perpetration of hate crime. Whilst current literature on hate crime predominantly looks at the impacts these crimes have on victims, it does not sufficiently investigate the conditions under which these crimes occur. The few studies on this issue have been conducted in the USA and Canada, the most recent of which was conducted by Perry and Scrivens (2019) who presented a new theoretical framework to account for this relationship. This framework - permission to hate - establishes a general environment of hate. This thesis contributes to the field by developing permission to hate to a more racialised social structure, and identifies the ways in which the radical right influences hate crime in England and Wales. Thus, this thesis is theory testing, adopting a similar sequential mixed-methods approach used by Perry and Scrivens. Due to the anti-minority ideology of the radical right, this thesis uses official crime statistics measuring racially and religiously aggravated crimes and demographic data to determine whether there is a correlation between these crimes and both the electoral performance of radical right parties and the protest activities of radial right organisations at the local level. In order to identify the causal mechanisms, a case study of the West Midlands is undertaken using semi-structured interviews with individuals who worked at Third Party Reporting Centres, media reports of radical right protests and more localised crime data. By combining these methods this thesis expands upon the work by Perry and Scrivens, contributing towards the theory permission to hate, whilst also highlighting the ways in which the radical right influence racially and religiously aggravated crimes. This study finds that the radical right achieve this through the consumption of alcohol during their protests, inserting themselves into local issues and how they emphasise the risks minority communities pose to British society, especially in the aftermath of high profile events

    Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts

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    Background: Personal recovery, ‘living a satisfying, hopeful and contributing lifeeven with the limitations caused by the illness’ (Anthony, 1993) is of particular value in bipolar disorder where symptoms often persist despite treatment. So far, personal recovery has only been studied in researcher-constructed environments (interviews, focus groups). Support forum posts can serve as a complementary naturalistic data source. Objective: The overarching aim of this thesis was to study personal recovery experiences that people living with bipolar disorder have shared in online support forums through integrating health research, NLP, and corpus linguistics in a mixed methods approach within a pragmatic research paradigm, while considering ethical issues and involving people with lived experience. Methods: This mixed-methods study analysed: 1) previous qualitative evidence on personal recovery in bipolar disorder from interviews and focus groups 2) who self-reports a bipolar disorder diagnosis on the online discussion platform Reddit 3) the relationship of mood and posting in mental health-specific Reddit forums (subreddits) 4) discussions of personal recovery in bipolar disorder subreddits. Results: A systematic review of qualitative evidence resulted in the first framework for personal recovery in bipolar disorder, POETIC (Purpose & meaning, Optimism & hope, Empowerment, Tensions, Identity, Connectedness). Mainly young or middle-aged US-based adults self-report a bipolar disorder diagnosis on Reddit. Of these, those experiencing more intense emotions appear to be more likely to post in mental health support subreddits. Their personal recovery-related discussions in bipolar disorder subreddits primarily focussed on three domains: Purpose & meaning (particularly reproductive decisions, work), Connectedness (romantic relationships, social support), Empowerment (self-management, personal responsibility). Support forum data highlighted personal recovery issues that exclusively or more frequently came up online compared to previous evidence from interviews and focus groups. Conclusion: This project is the first to analyse non-reactive data on personal recovery in bipolar disorder. Indicating the key areas that people focus on in personal recovery when posting freely and the language they use provides a helpful starting point for formal and informal carers to understand the concerns of people diagnosed with bipolar disorder and to consider how best to offer support

    Hierarchical Classification of Design Decisions using pre-trained Language Models

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    Die Software-Architektur Dokumentation (SAD) ist ein integrales Artefakt eines Software Projektes. Die SAD trĂ€gt zum fortwĂ€hrenden Erfolg eines Software Projektes bei, indem sie ein gemeinsames VerstĂ€ndnis der Software Architektur gewĂ€hrleistet, wichtige Entwurfsentscheidungen dokumentiert und einer Erosion der Software vorbeugt. Um die QualitĂ€t von SADs zu verbessern und nachgelagerte Aufgaben zu unterstĂŒtzen, ist eine automatische Klassifizierung dieser Entwurfsentscheidungen erstrebenswert. In dieser Arbeit implementieren und evaluieren wir einen Ansatz zur automatischen Identifikation und Klassifizierung von Entwurfsentscheidungen auf der Grundlage einer feingranularen Taxonomie, bei der wir eine hierarchische Klassifikationsstrategie mit dem Einsatz von Transfer-Lernen durch vortrainierter Sprachmodelle kombinieren. Der Beitrag dieser Arbeit besteht darin, den Vorteil einer hierarchischen Klassifikationsstrategie fĂŒr die automatische Klassifikation von Entwurfsentscheidungen gegenĂŒber einem nicht-hierarchischen Ansatz zu untersuchen. Außerdem untersuchen und vergleichen wir die EffektivitĂ€t der vortrainierten Sprachmodelle RoBERTa, XLNet, BERTOverflow und GPT-3 fĂŒr diese Aufgabe. In unserer Evaluation schnitten die AnsĂ€tze mit vortrainierten Sprachmodellen im Allgemeinen besser ab als die Baseline-AnsĂ€tze. Wir konnten jedoch keinen klaren Vorteil der hierarchischen AnsĂ€tze gegenĂŒber den nicht-hierarchischen AnsĂ€tzen in Kombination mit den Sprachmodellen feststelle. Letztlich sind die Ergebnisse dieser Arbeit durch die GrĂ¶ĂŸe und das Ungleichgewicht unseres Datensatzes limitiert und erfordern daher weitere Forschung mit einem grĂ¶ĂŸeren Datensatz

    Privacy Preserving Large Language Models: ChatGPT Case Study Based Vision and Framework

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    The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical private information such as, context, specific details, identifying information etc. This have raised serious threats to user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs that consists of two main components i.e., preserving user privacy during the data curation/pre-processing together with preserving private context and the private training process for large-scale data. To demonstrate its applicability, we show how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy; specifically, we employed differential privacy and private training using Reinforcement Learning (RL). We measure the privacy loss and evaluate the measure of uncertainty or randomness once differential privacy is applied. It further recursively evaluates the level of privacy guarantees and the measure of uncertainty of public database and resources, during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, we hypothetically compared other mechanisms e..g, Blockchain, private information retrieval, randomisation, for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility etc. We conclude that differential privacy, randomisation, and obfuscation can impact utility and performance of trained models, conversely, the use of ToR, Blockchain, and PIR may introduce additional computational complexity and high training latency. We believe that the proposed model could be used as a benchmark for proposing privacy preserving LLMs for generative AI tools

    A Primer on Seq2Seq Models for Generative Chatbots

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    The recent spread of Deep Learning-based solutions for Artificial Intelligence and the development of Large Language Models has pushed forwards significantly the Natural Language Processing area. The approach has quickly evolved in the last ten years, deeply affecting NLP, from low-level text pre-processing tasks –such as tokenisation or POS tagging– to high-level, complex NLP applications like machine translation and chatbots. This paper examines recent trends in the development of open-domain data-driven generative chatbots, focusing on the Seq2Seq architectures. Such architectures are compatible with multiple learning approaches, ranging from supervised to reinforcement and, in the last years, allowed to realise very engaging open-domain chatbots. Not only do these architectures allow to directly output the next turn in a conversation but, to some extent, they also allow to control the style or content of the response. To offer a complete view on the subject, we examine possible architecture implementations as well as training and evaluation approaches. Additionally, we provide information about the openly available corpora to train and evaluate such models and about the current and past chatbot competitions. Finally, we present some insights on possible future directions, given the current research status

    Understanding autistic communities need for substance use information: developing a pragmatic resource

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    Substance use and addiction are not typically first considered when discussing autism and the needs of autistic people. However, recent research has highlighted that autistic people do use substances and that they may be at an increased risk of developing substance-related problems compared to neurotypical people. Currently, there is very little to no support or information specifically tailored or targeted for autistic people, despite some motivations and presentations of substance-related problems being unique or highly specific to autistic communities (i.e., using substances for social camouflage). The current project aimed to address this by developing an online psychoeducational resource specifically tailored for and informed by autistic adults. The project was spilt into three stages, which adopted a pragmatic paradigm, was qualitative in nature and was analysed using summative qualitative content analysis and reflexive thematic analysis. The first stage encompassed the pre-development of the resource and consisted of an online structured interview, which sought to explore the preferences and needs for an educational resource on substance-related topics from autistic adults. The findings from this stage formed the basis for the second stage, which was the development of the resource itself. The final stage occurred post-development of the resource, which consisted of semi-structured interviews with autistic adults and parents of autistic people who provided feedback on the resource as well as discussions around their own experiences of substances. The resource developed was titled “Alcohol and Drug Information for Autistic Adults” (ADIFA: www.adifa.co.uk). This is currently, one of, if not, the only educational resource developed specifically for and informed by this community on substance-related topics. Autistic adults who participated in the project stated the importance of, and the need for, more pragmatic research to be undertaken in this area. Future directions for this project could be the quantitative evaluation of ADIFA for autistic adults and further research projects on substance-related issues in autistic communities, particularly employing participatory or co-production methodologies
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