Showing papers in "IEEE Computer in 2023"
TL;DR: In this article , the authors propose Wasabi, a conceptual model for trustworthy AI based on an adaptation of the well-known ability-benevolenceintegrity model of trust to trustworthiness.
Abstract: The expansion of artificial intelligence (AI) into our lives and livelihoods makes it clear that we must develop AI to be ethical and trustworthy. We propose Wasabi, a novel conceptual model for trustworthy AI based on an adaptation of the well-known ability–benevolence–integrity model of trust to trustworthiness.
6 citations
TL;DR: A survey of the main DL reliability assessment methodologies, focusing mainly on fault injection techniques used to evaluate DL resilience is presented in this article , where the authors present a survey of different reliability assessment methods.
Abstract: Deep learning (DL) reliability is becoming a growing concern, and efficient reliability assessment approaches are required to meet safety constraints. This article presents a survey of the main DL reliability assessment methodologies, focusing mainly on fault injection techniques used to evaluate DL resilience.
4 citations
TL;DR: In this paper , the authors examined the strategies of China, Saudi Arabia, South Korea, and the United Arab Emirates to grow the metaverse industry, and found that these strategies were ineffective.
Abstract: Many countries are viewing the metaverse as essential to their economies, developing national blueprints to grow the metaverse industry. This article examines said strategies of China, Saudi Arabia, South Korea, and the United Arab Emirates.
4 citations
TL;DR: The metaverse that is being created is starting from games but is not limited to games as mentioned in this paper , and there are scant educational programs to support this growing field, such as The Metaverse University.
Abstract: The metaverse that is being created is starting from games but is not limited to games. There are scant educational programs to support this growing field. In this article, we propose the founding of The Metaverse University.
3 citations
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TL;DR: In this paper , the January issue focuses on the topics that the guest editors feel are important going forward in 2023, focusing on the issues that they feel are worth mentioning in the future.
Abstract: The January issue focuses on the topics that the guest editors feel are important going forward in 2023.
3 citations
TL;DR: The field of quantum computers has reached the point of having real systems available for experimentation to a broad community as mentioned in this paper , and efforts to cultivate and educate a quantum-ready community are being made.
Abstract: The field of quantum computers has reached the point of having real systems available for experimentation to a broad community. In this article, we review recent developments in hardware technology, outline challenges and innovations in quantum systems, and discuss efforts to cultivate and educate a quantum-ready community.
2 citations
TL;DR: In this article , the authors discuss transparency obligations introduced in the Artificial Intelligence Act, the recently proposed European regulatory framework for artificial intelligence (AI), and present an analysis of the extent to which current approaches for AI documentation satisfy requirements and their suitability as a basis for future technical standards.
Abstract: This article discusses transparency obligations introduced in the Artificial Intelligence Act, the recently proposed European regulatory framework for artificial intelligence (AI). An analysis of the extent to which current approaches for AI documentation satisfy requirements is presented and their suitability as a basis for future technical standards is assessed.
2 citations
TL;DR: In this paper , the authors provide data scientists with motivation, theory, code, and examples on how to perform disciplined discovery of systematic deviations in data and models at the subset level.
Abstract: Trustworthy artificial intelligence researchers should seek to better detect and characterize systematic deviations in data and models (that is, bias). This article provides data scientists with motivation, theory, code, and examples on how to perform disciplined discovery of systematic deviations in data and models at the subset level.
2 citations
TL;DR: To improve the trustworthiness of artificial intelligence systems, organizations should address the risks relevant to the use case whilst taking into account the stakeholders that interact directly or indirectly with the system, this article .
Abstract: To improve the trustworthiness of artificial intelligence systems, organizations should address the risks relevant to the use case whilst taking into account the stakeholders that interact directly or indirectly with the system.
2 citations
TL;DR: In this paper , the authors provide data scientists with motivation, theory, code, and examples on how to perform disciplined discovery of systematic deviations in data and models at the subset level.
Abstract: Trustworthy artificial intelligence researchers should seek to better detect and characterize systematic deviations in data and models (that is, bias). This article provides data scientists with motivation, theory, code, and examples on how to perform disciplined discovery of systematic deviations in data and models at the subset level.
TL;DR: In this paper , the authors propose a framework for AI maintenance that facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.
Abstract: In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.
TL;DR: In this article , the authors describe research carried out by a U.K. consortium to address a central issue in establishing trustworthiness, namely verifiability, in autonomous systems.
Abstract: Autonomous systems have the promise to address many of our societal challenges in a variety of areas. To realize this potential, these systems need to be trustworthy. We describe research carried out by a U.K. consortium to address a central issue in establishing trustworthiness: verifiability.
TL;DR: In this article , the maturity level of different requirements for artificial intelligence (AI) in autonomous driving and the main challenges to be addressed in the future to ensure that automotive AI systems are developed in a trustworthy way are identified.
Abstract: We identify the maturity level of the different requirements for artificial intelligence (AI) in autonomous driving and outline the main challenges to be addressed in the future to ensure that automotive AI systems are developed in a trustworthy way.
TL;DR: In this article , the authors propose Wasabi, a conceptual model for trustworthy AI based on an adaptation of the well-known ability-benevolenceintegrity model of trust to trustworthiness.
Abstract: The expansion of artificial intelligence (AI) into our lives and livelihoods makes it clear that we must develop AI to be ethical and trustworthy. We propose Wasabi, a novel conceptual model for trustworthy AI based on an adaptation of the well-known ability–benevolence–integrity model of trust to trustworthiness.
TL;DR: In this article , the authors suggest two simple models to help guide appropriate analysis when concerns arise in the context of facial recognition technology, and propose a set of simple models for identifying relevant stakeholders.
Abstract: Power, accountability, and responsibility for facial recognition technology require us to think carefully and methodically about the ethically significant interests of stakeholders. We suggest two simple models to help guide appropriate analysis when concerns arise.
TL;DR: While we applaud the promise and value of GANs, leveraging their potential and values heavily depends on what we use them for and how, while also acknowledging and addressing their limits, limitations, and concerns as mentioned in this paper .
Abstract: While we applaud the promise and value of generative artificial intelligence and Chat Generative Pretrained Transformer–like tools, leveraging their potential and values heavily depends on what we use them for and how, while also acknowledging and addressing their limits, limitations, and concerns.
TL;DR: In this paper , the authors reviewed the key artificial intelligence breakthroughs made by two AI innovation giants, OpenAI and DeepMind, and found that they were the most successful ones.
Abstract: This article reviews the key artificial intelligence (AI) breakthroughs made by two AI innovation giants, OpenAI and DeepMind.
TL;DR: In this paper , a framework that provides multiple criteria, which could help start-ups strategically select freelancers is proposed to take advantage of freelancers as useful resources in requirement engineering activities.
Abstract: Resource-constrained start-up communities, faced with higher market failures, can take advantage of freelancers as useful resources in requirement engineering activities. This article proposes a framework that provides multiple criteria, which could help start-ups strategically select freelancers.
TL;DR: In this article , the authors suggest two simple models to help guide appropriate analysis when concerns arise in the context of facial recognition technology, and propose a set of simple models for identifying the most relevant stakeholders.
Abstract: Power, accountability, and responsibility for facial recognition technology require us to think carefully and methodically about the ethically significant interests of stakeholders. We suggest two simple models to help guide appropriate analysis when concerns arise.
TL;DR: In this article , the authors propose a framework for AI maintenance that facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.
Abstract: In this article, we carve out artificial intelligence (AI) maintenance from the robustness perspective. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems.
TL;DR: In this article , the authors discuss how technology can be used to solve environmental issues such as climate change and CO2 emissions, economical frictions such as energy supplies, and socio-political issues like wars threaten growth and equity.
Abstract: Sustainability has become a critical problem confronting the community. Ecological issues such as climate change and CO2 emissions, economical frictions such as energy supplies, and socio-political issues such as wars threaten growth and equity. How can technology help?
TL;DR: The global COVID-19 pandemic caused tremendous stress in many facets of our existence from global shifts to daily life-altering impacts as discussed by the authors , which caused many people and organizations to rethink how they worked.
Abstract: The global COVID-19 Pandemic caused tremendous stress in many facets of our existence from global shifts to daily life-altering impacts. Most significantly, the pandemic caused many people and organizations to rethink how they worked, that is, the pandemic was a forcing function.
TL;DR: In this article , the authors compare DBLP's indexed journals with those classified as CS by the leading indexing services today, Web of Science and Scopus, and conclude that the latter is more accurate than the former.
Abstract: DBLP provides comprehensive and free computer science (CS) bibliographic data to all. But how well does it really cover CS journals? In this article, we compare DBLP’s indexed journals with those classified as CS by the leading indexing services today—Web of Science and Scopus.
TL;DR: A time and location-based decryption algorithm using elliptic curve public-key cryptography was proposed in this article , which caters for a flexible and accurate range of decryption times and/or locations.
Abstract: Encrypting and decrypting documents is routinely performed by thousands of computers practically every moment. We describe a time- and location-based decryption algorithm using elliptic curve public-key cryptography that caters for a flexible and accurate range of decryption times and/or locations.
TL;DR: A time and location-based decryption algorithm using elliptic curve public-key cryptography was proposed in this article , which caters for a flexible and accurate range of decryption times and/or locations.
Abstract: Encrypting and decrypting documents is routinely performed by thousands of computers practically every moment. We describe a time- and location-based decryption algorithm using elliptic curve public-key cryptography that caters for a flexible and accurate range of decryption times and/or locations.
TL;DR: In this paper , the maturity level of different requirements for artificial intelligence (AI) in autonomous driving and the main challenges to be addressed in the future to ensure that automotive AI systems are developed in a trustworthy way are identified.
Abstract: We identify the maturity level of the different requirements for artificial intelligence (AI) in autonomous driving and outline the main challenges to be addressed in the future to ensure that automotive AI systems are developed in a trustworthy way.
TL;DR: In this paper , the authors review existing research for cooperative passive coherent location and tracking employing spaceborne opportunistic illuminators, highlighting the need for novel cooperative real-time object detection and tracking approaches and discussing the lack of multistatic experimental data for future research.
Abstract: This article reviews existing research for cooperative passive coherent location and tracking employing spaceborne opportunistic illuminators, highlighting the need for novel cooperative real-time object detection and tracking approaches and discussing the lack of multistatic experimental data for future research.
TL;DR: Decentralized identity offers a novel approach to address today’s identity challenges, putting users in control of their own digital identities and personal data as mentioned in this paper . But it is not suitable for large scale applications.
Abstract: As society increasingly relies on digital services, identity management becomes increasingly vital. Decentralized identity offers a novel approach to address today’s identity challenges, putting users in control of their own digital identities and personal data.
TL;DR: In this paper , redundancy and diversity of neural networks can help to reconcile DL technology and functional safety requirements in embedded critical systems, and they exemplify how redundancy can improve the functional safety.
Abstract: An increasing number of critical functionalities integrated in embedded critical systems rely on deep learning (DL) technology. This article summarizes certain key aspects of DL’s intrinsic stochastic and training-data-dependent nature that are at odds with current domain-specific functional safety standards. We exemplify how redundancy and diversity of neural networks can help to reconcile DL technology and functional safety requirements.