@article{AdnanSrsicVenticichetal.2020,
  author    = {Adnan, Hassan Sami and Srsic, Amanda and Venticich, Pete Milos and Townend, David M.R.},
  title     = {Using AI for mental health analysis and prediction in school surveys},
  series = {European journal of public health},
  volume    = {30},
  journal   = {European journal of public health},
  publisher = {Oxford Univ. Press},
  address   = {Oxford [u.a.]},
  issn      = {1101-1262},
  doi       = {10.1093/eurpub/ckaa165.336},
  pages     = {V125 -- V125},
  year      = {2020},
  abstract  = {Background: Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods: For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results: We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions: This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects.},
  language  = {en}
}
@article{MoellersHaelterlein2013,
  author    = {M{\"o}llers, Norma Tamaria and H{\"a}lterlein, Jens},
  title     = {Privacy issues in public discourse the case of "smart" CCTV in Germany},
  series = {Innovation : the European journal of social sciences},
  volume    = {26},
  journal   = {Innovation : the European journal of social sciences},
  number    = {1-2},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1351-1610},
  doi       = {10.1080/13511610.2013.723396},
  pages     = {57 -- 70},
  year      = {2013},
  abstract  = {In dealing with surveillance, scholars have widely agreed to refute privacy as an analytical concept and defining theme. Nonetheless, in public debates, surveillance technologies are still confronted with issues of privacy, and privacy therefore endures as an empirical subject of research on surveillance. Drawing from our analysis of public discourse of so-called smart closed-circuit television (CCTV) in Germany, we propose to use a sociology of knowledge perspective to analyze privacy in order to understand how it is socially constructed and negotiated. Our data comprise 117 documents, covering all publicly available documents between 2006 and 2010 that we were able to obtain. We found privacy to be the only form of critique in the struggle for the legitimate definition of smart CCTV. In this paper, we discuss the implications our preliminary findings have for the relationship between privacy issues and surveillance technology and conclude with suggestions of how this relationship might be further investigated as paradoxical, yet constitutive.},
  language  = {en}
}
@article{AckfeldRohloffRzepka2021,
  author    = {Ackfeld, Viola and Rohloff, Tobias and Rzepka, Sylvi},
  title     = {Increasing personal data contributions for the greater public good},
  series = {Behavioural public policy},
  journal   = {Behavioural public policy},
  publisher = {Cambridge University Press},
  address   = {Cambridge},
  issn      = {2398-063X},
  doi       = {10.1017/bpp.2021.39},
  pages     = {1 -- 27},
  year      = {2021},
  abstract  = {Personal data increasingly serve as inputs to public goods. Like other types of contributions to public goods, personal data are likely to be underprovided. We investigate whether classical remedies to underprovision are also applicable to personal data and whether the privacy-sensitive nature of personal data must be additionally accounted for. In a randomized field experiment on a public online education platform, we prompt users to complete their profiles with personal information. Compared to a control message, we find that making public benefits salient increases the number of personal data contributions significantly. This effect is even stronger when additionally emphasizing privacy protection, especially for sensitive information. Our results further suggest that emphasis on both public benefits and privacy protection attracts personal data from a more diverse set of contributors.},
  language  = {en}
}
@article{ErmakovaFabianZarnekow2016,
  author    = {Ermakova, Tatiana and Fabian, Benjamin and Zarnekow, Ruediger},
  title     = {Improving Individual Acceptance of Health Clouds through Confidentiality Assurance},
  series = {Applied clinical informatics},
  volume    = {7},
  journal   = {Applied clinical informatics},
  publisher = {Schattauer},
  address   = {Stuttgart},
  issn      = {1869-0327},
  doi       = {10.4338/ACI-2016-07-RA-0107},
  pages     = {983 -- 993},
  year      = {2016},
  abstract  = {Background: Cloud computing promises to essentially improve healthcare delivery performance. However, shifting sensitive medical records to third-party cloud providers could create an adoption hurdle because of security and privacy concerns. Methods: We empirically investigate our research question by a survey with over 260 full responses. For the setting with a high confidentiality assurance, we base on a recent multi-cloud architecture which provides very high confidentiality assurance through a secret-sharing mechanism: Health information is cryptographically encoded and distributed in a way that no single and no small group of cloud providers is able to decode it.},
  language  = {en}
}