4,615 research outputs found
Algorithmic personalization and brand loyalty: An experiential perspective
This article explores the relationship between algorithmic personalization and brand loyalty by examining how personalization experiences are articulated within the context of music streaming consumption. Despite previous acknowledgement of the link between personalization and brand loyalty, an experientially grounded understanding of how this works has yet to be articulated. Building upon the concept of ‘experiential brand loyalty’, the Algorithmic Personalization/Depersonalization Loop highlights the development of brand loyalty through consumers’ interactions with algorithm-backed brands. Being seen and understood by the algorithm sets off an iterative, two-way learning relationship that ultimately heightens the consumers’ experience, activates positive emotions, and deepens the relational bond with the brand, leading to brand loyalty. If, however, the algorithm is unsuccessful in personalizing the service experience, a ‘depersonalization’ process can occur that erodes brand loyalty and can lead to brand switching or even consumer activism
A survey on vulnerability of federated learning: A learning algorithm perspective
Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning
On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse
This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new
experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse
A survey on vulnerability of federated learning: A learning algorithm perspective
Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning
Systematic mapping of software engineering management with an agile approach
El enfoque ágil ha generado una amplia variedad de estrategias para administrar con éxito
diversos proyectos de software en todo el mundo. Además, podemos asegurar que los
proyectos de software se han beneficiado de los métodos ágiles ya conocidos. En este
sentido, este artÃculo busca demostrar cómo se aplica el enfoque ágil en las áreas de la
gestión en la ingenierÃa del Software. Para ello, este estudio realiza un mapeo sistemático
para identificar las principales tendencias en la gestión de la ingenierÃa de software con
un enfoque ágil. Se han identificado un total de 1137 artÃculos, de los cuales 165 son
relevantes para los fines de este estudio, estos indican que la entrega temprana de valor,
un principio clave de la agilidad, sigue siendo la principal tendencia para el uso de
métodos ágiles. Sin embargo, también existen fuertes tendencias enfocadas en puntos
clave de la gestión en ingenierÃa de software, como optimizar la gestión de calidad,
optimizar la especificación de requisitos, optimizar la gestión de riesgos y mejorar la
comunicación y coordinación del equipo, estos resultados permitirán generar nuevas
lÃneas de investigación para cada punto clave de la gestión en la ingenierÃa del software
impactado por el enfoque ágil.The agile approach has generated a wide variety of strategies to successfully manage
various software projects worldwide. In addition, we can ensure that software projects
have benefited from the already known agile methods. In this sense, this article seeks to
demonstrate how the agile approach is applied in Software engineering management
areas. To do this, this study performs a systematic mapping to identify the main trends in
software engineering management with an agile approach. A total of 1137 articles have
identified, of which 165 are relevant for the purposes of this study, these indicate that
early value delivery, a key principle of agility, continues to be the main trend for the use
of agile methods. However, there are also strong trends focused on key points of
management in software engineering, such as optimize quality management, optimize
requirements specification, optimize risk management, and improve team communication
and coordination, these results will allow generating new lines of research for each key
point of management in software engineering impacted by the agile approach
Technology and Contemporary Classical Music: Methodologies in Practice-Based Research
This position paper provides a distillation of the NCRM Innovation Forum, ‘Technology and Contemporary Classical Music: Methodologies in Creative Practice Research’, hosted by Cyborg Soloists in June 2023. It features contributions from a variety of creative practitioner-researchers to debate the current state and future of technologically focused, practice-based research in contemporary classical music.
The position paper is purposefully polyphonic and pluralistic. By collating a range of perspectives, experiences and expertise, the paper seeks to provoke and delineate a space for further questioning, inquiry, and response. The paper will be of interest to those working within creative practice research, particularly in relation to music, music technologists and those interested in research methodologies more broadly
Multidisciplinary perspectives on Artificial Intelligence and the law
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
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Democratic Fault Lines Then and Now: An Exploration of Longstanding and Emerging Threats to the Fulfillment of Democratic Expectations by the American Mass Public
Democratic theorists delineate several requirements for mass publics in democratic societies. These include holding policy preferences, deliberating over competing viewpoints, and making informed choices. This dissertation contributes to debates about the public’s performance in each of these areas.
In the first chapter, I argue that a statistical method that has been used to characterize the public’s ideological consistency has produced misleading results. In the second, I demonstrate that two aspects of Americans’ social networks differ in their relationships to important political attitudes necessary for productive deliberation. In the third, I show that Americans with politically diverse social networks trust more of the content they encounter on social media but are no more likely to discern truth from falsehood or respond to accuracy nudging interventions. In total, this dissertation employs analytical, observational, and experimental research methods to address questions that concern old and new threats to mass democratic behavior in the United States
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