Making Sense of the World: Framing Models for Trustworthy Sensor-Driven Systems

Calder, Muffy; Dobson, Simon; Fisher, Michael; McCann, Julie A.

doi:10.3390/computers7040062

Cited by 4 publications

(6 citation statements)

References 23 publications

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“…This approach, however, is not without obstacles: "a challenge common to all emerging collaborative environments that promote open science and the rapid exchange of experimental and prepublication data and methods is one of trust" [10, p. 609]. Taking into account multiple stakeholders and their concerns imports a consideration of a multiplicity of trusts within the design of Virtual Labs [23,74,115,141,145]. For instance, the promotion of trust is related to: the adoption and use of Virtual Labs; the research outputs included within the lab itself; and other stakeholders using the Virtual Lab.…”

Section: The Challenge Of Trustmentioning

confidence: 99%

“…The provision of documentation may already satisfy users, but when it does not, other scrutability-supporting features are needed. The ability to ask questions and receive reliable answers can enable stakeholders to withhold or place trust [23]. This raises certain questions: what questions do people need answers to and what evidence (or components) would serve to answer these questions?…”

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confidence: 99%

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Fifty Shades of Grey

Thornton

Knowles

Blair

2021

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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Environmental data science is uniquely placed to respond to essentially complex and fantastically worthy challenges related to arresting planetary destruction. Trust is needed for facilitating collaboration between scientists who may share datasets and algorithms, and for crafting appropriate science-based policies. Achieving this trust is particularly challenging because of the numerous complexities, multi-scale variables, interdependencies and multi-level uncertainties inherent in environmental data science. Virtual Labs-easily accessible online environments provisioning access to datasets, analysis and visualisations-are socio-technical systems which, if carefully designed, might address these challenges and promote trust in a variety of ways. In addition to various system properties that can be utilised in support of effective collaboration, certain features which are commonly seen to benefit trust-transparency and provenance in particular-appear applicable to promoting trust in and through Virtual Labs. Attempting to realise these features in their design reveals, however, that their implementation is more nuanced and complex than it would appear. Using the lens of affordances, we argue for the need to carefully articulate these features, with consideration of multiple stakeholder needs on balance, so that these Virtual Labs do in fact promote trust. We argue that these features not be conceived as widgets that can be imported into a given context to promote trust; rather, whether they promote trust is a function of how systematically designers consider various (potentially conflicting) stakeholder trust needs. CCS CONCEPTS• Human-centered computing → Computer supported cooperative work; HCI theory, concepts and models.

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Section: The Challenge Of Trustmentioning

confidence: 99%

mentioning

confidence: 99%

Fifty Shades of Grey

Thornton

Knowles

Blair

2021

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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“…Stakeholders often have very different perspectives on the key abstractions and assumptions about the system being modelled. Frames of reference [ 8 ] are one way of articulating the variety of perspectives, and their context. Clarity on frames allows different levels and type of concern to be balanced within model development and analysis, driving the selection of model type and techniques.…”

Section: Types Of Model and Analysismentioning

confidence: 99%

Computational modelling for decision-making: where, why, what, who and how

et al. 2018

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In order to deal with an increasingly complex world, we need ever more sophisticated computational models that can help us make decisions wisely and understand the potential consequences of choices. But creating a model requires far more than just raw data and technical skills: it requires a close collaboration between model commissioners, developers, users and reviewers. Good modelling requires its users and commissioners to understand more about the whole process, including the different kinds of purpose a model can have and the different technical bases. This paper offers a guide to the process of commissioning, developing and deploying models across a wide range of domains from public policy to science and engineering. It provides two checklists to help potential modellers, commissioners and users ensure they have considered the most significant factors that will determine success. We conclude there is a need to reinforce modelling as a discipline, so that misconstruction is less likely; to increase understanding of modelling in all domains, so that the misuse of models is reduced; and to bring commissioners closer to modelling, so that the results are more useful.

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“…Describing such varying environments and systematically dealing with uncertainty represent further key challenges that have been addressed in such European research projects as Pegasus 31 and the U.K. EPSRC-funded S4: Science for Sensor Systems in 2016. 32 Safety and security engineering concerns the connectivity and spread of CPS and provides new attack surfaces that could exploit vulnerabilities in the cyber and/or physical side, as well as among human stakeholders. This implies that existing security approaches are not suitable.…”

Section: Trust Safety Security Privacy (Guarantees)mentioning

confidence: 99%

Connected things connecting Europe

et al. 2019

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It is estimated that personal computers, data centers, and other technologies constitute less than 1% of all microprocessor usage; 10 embedded systems represent the remaining percentage and can be found in our washing machines, microwaves, remote controls, PC peripherals (such as keyboards and mobile phones), with modern cars containing many tens of embedded microcontrollers. 18 Modern embedded-system microcontroller and transceiver technology advancements have brought forth the kinds of systems we have in the past defined as pervasive, ubiquitous, and embedded computing, and for some time in Europe, "embedded intelligence." However, today they are better known as the Internet of Things (IoT) and cyber-physical systems (CPS); see the figure here.The jury is still out regarding a definition of the latter two terms or indeed how to differentiate them, but on the whole people tend to refer to IoT as embedded devices that connect to the Internet to exchange data, optimize processes, monitor environments, and typically consist of sensors, actuators, and low-power compute infrastructures. CPS is a term first coined in 2006 in the U.S. to characterize "the integration of physical systems and processes with networked computing" for systems that "use computations and communication deeply embedded in and interacting with physical processes to add new capabilities to physical systems." 22 CPS is generally put forward as the more systems notion, while IoT emphasizes communication and analytics, yet IoT-like devices need not use Internet protocols to create a CPS, hence the ambiguity. The European Commission debated for two years whether to call its embedded intelligence programs CPS, with the latter winning out in the end. In this article, we embrace these terms fluidly and name them IoT/CPS; for other definitions see the sidebar "Some Definitions.

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Making Sense of the World: Framing Models for Trustworthy Sensor-Driven Systems

Cited by 4 publications

References 23 publications

Fifty Shades of Grey

Fifty Shades of Grey

Computational modelling for decision-making: where, why, what, who and how

Connected things connecting Europe

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