Social network analytics, data science ethics & privacy-preserving analytics

Author(s):  
Dino Pedreschi
2015 ◽  
Vol 47 (3) ◽  
pp. 595-623 ◽  
Author(s):  
Yongjiao Sun ◽  
Ye Yuan ◽  
Guoren Wang ◽  
Yurong Cheng

2013 ◽  
Vol 19 (7) ◽  
pp. 1095-1108 ◽  
Author(s):  
A. Perer ◽  
I. Guy ◽  
E. Uziel ◽  
I. Ronen ◽  
M. Jacovi

2018 ◽  
Vol 129 ◽  
pp. 368-371 ◽  
Author(s):  
Lina Ni ◽  
Yanfeng Yuan ◽  
Xiao Wang ◽  
Mengmeng Zhang ◽  
Jinquan Zhang

2018 ◽  
Vol 8 (4) ◽  
pp. 60 ◽  
Author(s):  
Oana Fodor ◽  
Alina Fleștea ◽  
Iulian Onija ◽  
Petru Curșeu

Multiparty systems (MPSs) are defined as collaborative task-systems composed of various stakeholders (organizations or their representatives) that deal with complex issues that cannot be addressed by a single group or organization. Our study uses a behavioral simulation in which six stakeholder groups engage in interactions in order to reach a set of agreements with respect to complex educational policies. We use a social network perspective to explore the dynamics of network centrality during intergroup interactions in the simulation and show that trust self-enhancement at the onset of the simulation has a positive impact on the evolution of network centrality throughout the simulation. Our results have important implications for the social networks dynamics in MPSs and point towards the benefit of using social network analytics as exploration and/or facilitating tools in MPSs.


Author(s):  
Cat Drew

Data science can offer huge opportunities for government. With the ability to process larger and more complex datasets than ever before, it can provide better insights for policymakers and make services more tailored and efficient. As with all new technologies, there is a risk that we do not take up its opportunities and miss out on its enormous potential. We want people to feel confident to innovate with data. So, over the past 18 months, the Government Data Science Partnership has taken an open, evidence-based and user-centred approach to creating an ethical framework. It is a practical document that brings all the legal guidance together in one place, and is written in the context of new data science capabilities. As part of its development, we ran a public dialogue on data science ethics, including deliberative workshops, an experimental conjoint survey and an online engagement tool. The research supported the principles set out in the framework as well as provided useful insight into how we need to communicate about data science. It found that people had a low awareness of the term ‘data science’, but that showing data science examples can increase broad support for government exploring innovative uses of data. But people's support is highly context driven. People consider acceptability on a case-by-case basis, first thinking about the overall policy goals and likely intended outcome, and then weighing up privacy and unintended consequences. The ethical framework is a crucial start, but it does not solve all the challenges it highlights, particularly as technology is creating new challenges and opportunities every day. Continued research is needed into data minimization and anonymization, robust data models, algorithmic accountability, and transparency and data security. It also has revealed the need to set out a renewed deal between the citizen and state on data, to maintain and solidify trust in how we use people's data for social good. This article is part of the themed issue ‘The ethical impact of data science’.


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