Bots as Virtual Confederates

Krafft, Peter M.; Macy, Michael W.; Pentland, Alex

doi:10.1145/2998181.2998354

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…Fifth, the study of machine behaviour will often require experimental intervention to study human-machine interactions in real-world settings 142,143 . These interventions could alter the overall behaviour of the system, possibly having adverse effects on normal users 144 .…”

Section: Review Researchmentioning

confidence: 99%

Machine behaviour

Rahwan

Cebrián

Obradovich

et al. 2019

Nature

831

436

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Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.

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Section: Review Researchmentioning

confidence: 99%

Machine behaviour

Rahwan

Cebrián

Obradovich

et al. 2019

Nature

831

436

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show abstract

“…While this study could not eliminate endogeneity within user networks, the randomization of messages allowed for suggestive experimental results on the ways that social exposure to messages influence user behavior. 1 Another related approach to experimentation on social media comes from the advent of experimental methods that use algorithmically controlled social media accounts called bots to manipulate users' experiences (Krafft et al 2016). Mønsted et al (2017) released a network of bots into twitter and tested whether they could prompt the uptake of specific hashtags.…”

Section: Social Mediamentioning

confidence: 99%

Complex Contagions: A Decade in Review

Guilbeault

Becker

Centola

2018

Computational Social Sciences

136

104

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Since the publication of "Complex Contagions and the Weakness of Long Ties" in 2007, complex contagions have been studied across an enormous variety of social domains. In reviewing this decade of research, we discuss recent advancements in applied studies of complex contagions, particularly in the domains of health, innovation diffusion, social media, and politics. We also discuss how these empirical studies have spurred complementary advancements in the theoretical modeling of contagions, which concern the effects of network topology on diffusion, as well as the effects of individual-level attributes and thresholds. In synthesizing these developments, we suggest three main directions for future research. The first concerns the study of how multiple contagions interact within the same network and across networks, in what may be called an ecology of contagions. The second concerns the study of how the structure of thresholds and their behavioral consequences can vary by individual and social context. The third area concerns the roles of diversity and homophily in the dynamics of complex contagion, including both diversity of demographic profiles among local peers, and the broader notion of structural diversity within a network. Throughout this discussion, we make an effort to highlight the theoretical and empirical opportunities that lie ahead.

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“…As VLC research falls into the broad category of computational behavioural science, it must follow the Belmont Report principles in that VLCs should not violate learners' personal Towards AI-powered personalization in MOOC learning H Yu et al autonomy, benefits brought by the VLCs should outweigh risks, and the benefits and risks should be distributed fairly among learners. 46 As VLCs may be designed to help learners adopt more effective learning habits through learning skill analytics and smart interventions, such applications might be perceived as violating learners' personal autonomy. Thus, mechanisms to obtain informed consent from learners will be needed in MOOC platforms which make use of VLCs.…”

Section: Personalizing Learning Supportmentioning

confidence: 99%

Towards AI-powered personalization in MOOC learning

Chen

Leung

et al. 2017

npj Science Learn

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Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to MOOC mediation tasks to offer personal attention to learners; and how virtual learning companions with human traits such as curiosity and emotions can enhance learning experience on a large scale. These new capabilities will also bring opportunities for educational researchers to analyse students' learning skills and uncover points along learning paths where students with different backgrounds may require different help. Ethical considerations related to the application of AI in MOOC education research are also discussed.

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Bots as Virtual Confederates

Cited by 21 publications

References 25 publications

Machine behaviour

Machine behaviour

Complex Contagions: A Decade in Review

Towards AI-powered personalization in MOOC learning

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