Machine Behaviour (Originally Published 2019 by Springer Nature)

Rahwan, Iyad; Cebrián, Manuel; Obradovich, Nick; Bongard, Josh; Bonnefon, Jean‐François; Breazeal, Cynthia; Crandall, Jacob W.; Christakis, Nicholas A.; Couzin, Iain D.; Jackson, Matthew O.; Jennings, Nicholas R.; Kamar, Ece; Kloumann, Isabel M.; Larochelle, Hugo; Lazer, David; McElreath, Richard; Mislove, Alan; Parkes, David C.; Pentland, Alex; Roberts, Margaret E.; Tenenbaum, Joshua B.; Wellman, Michael P.

doi:10.1002/9781119815075.ch10

“…Users influence ddA when explicitly generating data or performing actions that are implicitly collected, impacting the outputs and digital doppelgänger through data processing. Conversely, a ddA affects users (as discussed in Tufekci, 2014;Kramer et al, 2014;Rahwan et al, 2019;Susser et al, 2019). For example, they can effect behaviour and decision-making as provoked by nudging and other techniques of attention engineering.…”

Section: Perspective On Interactions With Data-driven Digital Artefactsmentioning

confidence: 99%

The data awareness framework as part of data literacies in K-12 education

Höper,

Schulte

2023

ILS

0

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Purpose In today’s digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students’ data awareness. Design/methodology/approach The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students’ awareness and understanding of and reflection on data practices of data-driven digital artefacts. Findings The study’s findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour. Originality/value Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.

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“…Cooperative relationships between humans and machines were envisioned back in 1960 [39,65]. This work defines machines in a broad sense, considering intelligent systems that can make decisions autonomously and independently (e.g., automated, autonomous, or AI agents, robots, vehicles, and instruments) [39,83,114]. Relationships between humans and machines have been characterized through different theories, such as the Socio-Technical Systems theory (considers humans and technology shape each other while pursuing a common goal within an organization), Actor-Network theory (considers machines should be equally pondered by humans when analyzing a social system, considering the later as an association of heterogeneous elements), Cyber-Physical Social Systems theory (extends the Socio-Technical Systems theory emphasizing social dimensions where computational algorithms are used to monitor devices), the theory on social machines (considers systems that combine social participation with machine-based computation), and the Human-Machine Networks theory (considers humans and machines form interdependent networks characterized by synergistic interactions).…”

Section: Overview On Human-machine Collaborationmentioning

confidence: 99%

Human in the AI Loop via xAI and Active Learning for Visual Inspection

Rožanec,

Montini,

Cutrona

et al. 2023

Artificial Intelligence in Manufacturing

0

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Industrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human-digital twins, and cybersecurity.

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“…Cooperative relationships between humans and machines were envisioned back in 1960 [39,65]. This work defines machines in a broad sense, considering intelligent systems that can make decisions autonomously and independently (e.g., automated, autonomous, or AI agents, robots, vehicles, and instruments) [39,83,114]. Relationships between humans and machines have been characterized through different theories, such as the Socio-Technical Systems theory (considers humans and technology shape each other while pursuing a common goal within an organization), Actor-Network theory (considers machines should be equally pondered by humans when analyzing a social system, considering the later as an association of heterogeneous elements), Cyber-Physical Social Systems theory (extends the Socio-Technical Systems theory emphasizing social dimensions where computational algorithms are used to monitor devices), the theory on social machines (considers systems that combine social participation with machine-based computation), and the Human-Machine Networks theory (considers humans and machines form interdependent networks characterized by synergistic interactions).…”

Section: Overview On Human-machine Collaborationmentioning

confidence: 99%

Artificial Intelligence in Manufacturing

Chen

¹

,

Wang

²

2022

Artificial Intelligence and Lean Manufacturing

4

0

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Machine Behaviour (Originally Published 2019 by Springer Nature)

Cited by 10 publications

References 121 publications

The data awareness framework as part of data literacies in K-12 education

The data awareness framework as part of data literacies in K-12 education

Human in the AI Loop via xAI and Active Learning for Visual Inspection

Artificial Intelligence in Manufacturing

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