Trustworthy AI in the Age of Pervasive Computing and Big Data

Kumar, Abhishek; Braud, Tristan; Tarkoma, Sasu; Hui, Pan

doi:10.1109/percomworkshops48775.2020.9156127

Cited by 47 publications

(23 citation statements)

References 38 publications

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“…A systematic literature review approach was employed in selecting relevant literature from a number of key areas that represent some notable present-day computing ethics topics and challenges (similar reviews have been undertaken by researchers such as Braunack-Mayer et al , 2020; Saltz et al , 2019; Saltz and Dewar, 2019). These key areas, also highlighted by Kumar et al (2020), focus on the overlap between three key areas in contemporary computing ethics, the areas of data science, AI and pervasive computing (including surveillance and privacy), thus the focus of this literature review is to critically examine those three areas, and to explore the themes that have emerged in each of those domains in the past five years.…”

Section: Literature Reviewmentioning

confidence: 99%

Perspectives on computing ethics: a multi-stakeholder analysis

Gordon

Stavrakakis

Gibson

et al. 2021

JICES

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Purpose Computing ethics represents a long established, yet rapidly evolving, discipline that grows in complexity and scope on a near-daily basis. Therefore, to help understand some of that scope it is essential to incorporate a range of perspectives, from a range of stakeholders, on current and emerging ethical challenges associated with computer technology. This study aims to achieve this by using, a three-pronged, stakeholder analysis of Computer Science academics, ICT industry professionals, and citizen groups was undertaken to explore what they consider to be crucial computing ethics concerns. The overlap between these stakeholder groups are explored, as well as whether their concerns are reflected in the existing literature. Design/methodology/approach Data collection was performed using focus groups, and the data was analysed using a thematic analysis. The data was also analysed to determine if there were overlaps between the literature and the stakeholders’ concerns and attitudes towards computing ethics. Findings The results of the focus group analysis show a mixture of overlapping concerns between the different groups, as well as some concerns that are unique to each of the specific groups. All groups stressed the importance of data as a key topic in computing ethics. This includes concerns around the accuracy, completeness and representativeness of data sets used to develop computing applications. Academics were concerned with the best ways to teach computing ethics to university students. Industry professionals believed that a lack of diversity in software teams resulted in important questions not being asked during design and development. Citizens discussed at length the negative and unexpected impacts of social media applications. These are all topics that have gained broad coverage in the literature. Social implications In recent years, the impact of ICT on society and the environment at large has grown tremendously. From this fast-paced growth, a myriad of ethical concerns have arisen. The analysis aims to shed light on what a diverse group of stakeholders consider the most important social impacts of technology and whether these concerns are reflected in the literature on computing ethics. The outcomes of this analysis will form the basis for new teaching content that will be developed in future to help illuminate and address these concerns. Originality/value The multi-stakeholder analysis provides individual and differing perspectives on the issues related to the rapidly evolving discipline of computing ethics.

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Section: Literature Reviewmentioning

confidence: 99%

Perspectives on computing ethics: a multi-stakeholder analysis

Gordon

Stavrakakis

Gibson

et al. 2021

JICES

View full text Add to dashboard Cite

show abstract

“…As a result, communication costs such as latency are significantly reduced while learning qualities are ensured. FL for mobile pervasive computing in smart cities is also considered in [155]. For example, vehicles can join an FL system to collaboratively train AI models for prediction of locations of charger installations without revealing information to RSUs.…”

Section: Fl For Smart Citymentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

594

182

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“…In terms of AI itself, the regulation is currently very sparse but developing. For example, the EU recently released a draft paper outlining its vision for AI regulation in high-risk areas (e.g., transportation or health care) [64], [65], as well as ethical guidelines for building trustworthy AI systems [66], [67]. The draft includes an overarching framework that would cover the training data (including data traceability and coverage to ensure fairness), model explainability (to understand why certain decisions are taken), and liability (in case of harm).…”

Section: B Ai Regulationmentioning

confidence: 99%