3,376 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    The ethics of ChatGPT – Exploring the ethical issues of an emerging technology

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    This article explores ethical issues raised by generative conversational AI systems like ChatGPT. It applies established approaches for analysing ethics of emerging technologies to undertake a systematic review of possible benefits and concerns. The methodology combines ethical issues identified by Anticipatory Technology Ethics, Ethical Impact Assessment, and Ethical Issues of Emerging ICT Applications with AI-specific issues from the literature. These are applied to analyse ChatGPT's capabilities to produce humanlike text and interact seamlessly. The analysis finds ChatGPT could provide high-level societal and ethical benefits. However, it also raises significant ethical concerns across social justice, individual autonomy, cultural identity, and environmental issues. Key high-impact concerns include responsibility, inclusion, social cohesion, autonomy, safety, bias, accountability, and environmental impacts. While the current discourse focuses narrowly on specific issues such as authorship, this analysis systematically uncovers a broader, more balanced range of ethical issues worthy of attention. Findings are consistent with emerging research and industry priorities on ethics of generative AI. Implications include the need for diverse stakeholder engagement, considering benefits and risks holistically when developing applications, and multi-level policy interventions to promote positive outcomes. Overall, the analysis demonstrates that applying established ethics of technology methodologies can produce a rigorous, comprehensive foundation to guide discourse and action around impactful emerging technologies like ChatGPT. The paper advocates sustaining this broad, balanced ethics perspective as use cases unfold to realize benefits while addressing ethical downsides

    Gender bias in transformers: A comprehensive review of detection and mitigation strategies

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    Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound implications for individuals’ lives. This paper presents a comprehensive survey that explores gender bias in Transformer models from a linguistic perspective. While the existence of gender bias in language models has been acknowledged in previous studies, there remains a lack of consensus on how to measure and evaluate this bias effectively. Our survey critically examines the existing literature on gender bias in Transformers, shedding light on the diverse methodologies and metrics employed to assess bias. Several limitations in current approaches to measuring gender bias in Transformers are identified, encompassing the utilization of incomplete or flawed metrics, inadequate dataset sizes, and a dearth of standardization in evaluation methods. Furthermore, our survey delves into the potential ramifications of gender bias in Transformers for downstream applications, including dialogue systems and machine translation. We underscore the importance of fostering equity and fairness in these systems by emphasizing the need for heightened awareness and accountability in developing and deploying language technologies. This paper serves as a comprehensive overview of gender bias in Transformer models, providing novel insights and offering valuable directions for future research in this critical domain

    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

    Undergraduate Catalog of Studies, 2022-2023

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    Smart object-oriented access control: Distributed access control for the Internet of Things

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    Ensuring that data and devices are secure is of critical importance to information technology. While access control has held a key role in traditional computer security, its role in the evolving Internet of Things is less clear. In particular, the access control literature has suggested that new challenges, such as multi-user controls, fine-grained controls, and dynamic controls, prompt a foundational re-thinking of access control. We analyse these challenges, finding instead that the main foundational challenge posed by the Internet of Things involves decentralization: accurately describing access control in Internet of Things environments (e.g., the Smart Home) requires a new model of multiple, independent access control systems. To address this challenge, we propose a meta-model (i.e., a model of models): Smart Object-Oriented Access Control (SOOAC). This model is an extension of the XACML framework, built from principles relating to modularity adapted from object-oriented programming and design. SOOAC draws attention to a new class of problem involving the resolution of policy conflicts that emerge from the interaction of smart devices in the home. Contrary to traditional (local) policy conflicts, these global policy conflicts emerge when contradictory policies exist across multiple access control systems. We give a running example of a global policy conflict involving transitive access. To automatically avoid global policy conflicts before they arise, we extend SOOAC with a recursive algorithm through which devices communicate access requests before allowing or denying access themselves. This algorithm ensures that both individual devices and the collective smart home are secure. We implement SOOAC within a prototype smart home and assess its validity in terms of effectiveness and efficiency. Our analysis shows that SOOAC is successful at avoiding policy conflicts before they emerge, in real time. Finally, we explore improvements that can be made to SOOAC and suggest directions for future work

    Deteção de intrusões de rede baseada em anomalias

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    Dissertação de mestrado integrado em Eletrónica Industrial e ComputadoresAo longo dos últimos anos, a segurança de hardware e software tornou-se uma grande preocupação. À medida que a complexidade dos sistemas aumenta, as suas vulnerabilidades a sofisticadas técnicas de ataque têm proporcionalmente escalado. Frequentemente o problema reside na heterogenidade de dispositivos conectados ao veículo, tornando difícil a convergência da monitorização de todos os protocolos num único produto de segurança. Por esse motivo, o mercado requer ferramentas mais avançadas para a monitorizar ambientes críticos à vida humana, tais como os nossos automóveis. Considerando que existem várias formas de interagir com os sistemas de entretenimento do automóvel como o Bluetooth, o Wi-fi ou CDs multimédia, a necessidade de auditar as suas interfaces tornou-se uma prioridade, uma vez que elas representam um sério meio de aceeso à rede interna do carro. Atualmente, os mecanismos de segurança de um carro focam-se na monitotização da rede CAN, deixando para trás as tecnologias referidas e não contemplando os sistemas não críticos. Como exemplo disso, o Bluetooth traz desafios diferentes da rede CAN, uma vez que interage diretamente com o utilizador e está exposto a ataques externos. Uma abordagem alternativa para tornar o automóvel num sistema mais robusto é manter sob supervisão as comunicações que com este são estabelecidas. Ao implementar uma detecção de intrusão baseada em anomalias, esta dissertação visa analisar o protocolo Bluetooth no sentido de identificar interações anormais que possam alertar para uma situação fora dos padrões de utilização. Em última análise, este produto de software embebido incorpora uma grande margem de auto-aprendizagem, que é vital para enfrentar quaisquer ameaças desconhecidas e aumentar os níveis de segurança globais. Ao longo deste documento, apresentamos o estudo do problema seguido de uma metodologia alternativa que implementa um algoritmo baseado numa LSTM para prever a sequência de comandos HCI correspondentes a tráfego Bluetooth normal. Os resultados mostram a forma como esta abordagem pode impactar a deteção de intrusões nestes ambientes ao demonstrar uma grande capacidade para identificar padrões anómalos no conjunto de dados considerado.In the last few years, hardware and software security have become a major concern. As the systems’ complexity increases, its vulnerabilities to several sophisticated attack techniques have escalated likewise. Quite often, the problem lies in the heterogeneity of the devices connected to the vehicle, making it difficult to converge the monitoring systems of all existing protocols into one security product. Thereby, the market requires more refined tools to monitor life-risky environments such as personal vehicles. Considering that there are several ways to interact with the car’s infotainment system, such as Wi-fi, Bluetooth, or CD player, the need to audit these interfaces has become a priority as they represent a serious channel to reach the internal car network. Nowadays, security in car networks focuses on CAN bus monitoring, leaving behind the aforementioned technologies and not contemplating other non-critical systems. As an example of these concerns, Bluetooth brings different challenges compared to CAN as it interacts directly with the user, being exposed to external attacks. An alternative approach to converting modern vehicles and their set of computers into more robust systems is to keep track of established communications with them. By enforcing anomaly-based intrusion detection this dissertation aims to analyze the Bluetooth protocol to identify abnormal user interactions that may alert for a non conforming pattern. Ultimately, such embedded software product incorporates a self-learning edge, which is vital to face newly developed threats and increasing global security levels. Throughout this document, we present the study case followed by an alternative methodology that implements an LSTM based algorithm to predict a sequence of HCI commands corresponding to normal Bluetooth traffic. The results show how this approach can impact intrusion detection in such environments by expressing a high capability of identifying abnormal patterns in the considered data

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023
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