TY - JOUR A1 - Ackfeld, Viola A1 - Rohloff, Tobias A1 - Rzepka, Sylvi T1 - Increasing personal data contributions for the greater public good BT - a field experiment on an online education platform JF - Behavioural public policy N2 - 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. KW - field experiment KW - personal data KW - public good KW - privacy Y1 - 2021 U6 - https://doi.org/10.1017/bpp.2021.39 SN - 2398-063X SN - 2398-0648 SP - 1 EP - 27 PB - Cambridge University Press CY - Cambridge ER - TY - CHAP A1 - Adnan, Hassan Sami A1 - Srsic, Amanda A1 - Venticich, Pete Milos A1 - Townend, David M.R. T1 - Using AI for mental health analysis and prediction in school surveys T2 - European journal of public health N2 - 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. KW - ethics KW - artificial intelligence KW - adolescent KW - child KW - confidentiality KW - health personnel KW - mental disorders KW - mental health KW - personal satisfaction KW - privacy KW - school (environment) KW - statutes and laws KW - public health medicine KW - surveillance KW - medical KW - prevention KW - datasets KW - machine learning KW - supervised machine learning KW - data analysis Y1 - 2020 U6 - https://doi.org/10.1093/eurpub/ckaa165.336 SN - 1101-1262 SN - 1464-360X VL - 30 SP - V125 EP - V125 PB - Oxford Univ. Press CY - Oxford [u.a.] ER - TY - CHAP A1 - Diaz Ferreyra, Nicolás Emilio A1 - Shahi, Gautam Kishore A1 - Tony, Catherine A1 - Stieglitz, Stefan A1 - Scandariato, Riccardo ED - Schmidt, Albrecht ED - Väänänen, Kaisa ED - Goyal, Tesh ED - Kristensson, Per Ola ED - Peters, Anicia T1 - Regret, delete, (do not) repeat BT - an analysis of self-cleaning practices on twitter after the outbreak of the covid-19 pandemic T2 - Extended abstracts of the 2023 CHI conference on human factors in computing systems N2 - During the outbreak of the COVID-19 pandemic, many people shared their symptoms across Online Social Networks (OSNs) like Twitter, hoping for others’ advice or moral support. Prior studies have shown that those who disclose health-related information across OSNs often tend to regret it and delete their publications afterwards. Hence, deleted posts containing sensitive data can be seen as manifestations of online regrets. In this work, we present an analysis of deleted content on Twitter during the outbreak of the COVID-19 pandemic. For this, we collected more than 3.67 million tweets describing COVID-19 symptoms (e.g., fever, cough, and fatigue) posted between January and April 2020. We observed that around 24% of the tweets containing personal pronouns were deleted either by their authors or by the platform after one year. As a practical application of the resulting dataset, we explored its suitability for the automatic classification of regrettable content on Twitter. KW - privacy KW - self-disclosure KW - online regrets KW - deleted tweets KW - crisis communication KW - COVID-19 Y1 - 2023 SN - 978-1-45039-422-2 U6 - https://doi.org/10.1145/3544549.3585583 SP - 1 EP - 7 PB - ACM CY - New York, NY ER - TY - CHAP A1 - Ermakova, Tatiana A1 - Fabian, Benjamin A1 - Bender, Benedict A1 - Klimek, Kerstin T1 - Web Tracking BT - a literature review on the state of research T2 - Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS 51) N2 - Web tracking seems to become ubiquitous in online business and leads to increased privacy concerns of users. This paper provides an overview over the current state of the art of web-tracking research, aiming to reveal the relevance and methodologies of this research area and creates a foundation for future work. In particular, this study addresses the following research questions: What methods are followed? What results have been achieved so far? What are potential future research areas? For these goals, a structured literature review based upon an established methodological framework is conducted. The identified articles are investigated with respect to the applied research methodologies and the aspects of web tracking they emphasize. KW - Information Security and Privacy KW - literature review KW - privacy KW - web-tracking Y1 - 2018 U6 - https://doi.org/10.24251/HICSS.2018.596 SN - 2572-6862 SP - 4732 EP - 4741 PB - HICSS Conference Office University of Hawaii at Manoa CY - Maile Way ER - TY - JOUR A1 - Ermakova, Tatiana A1 - Fabian, Benjamin A1 - Zarnekow, Ruediger T1 - Improving Individual Acceptance of Health Clouds through Confidentiality Assurance JF - Applied clinical informatics N2 - 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. KW - Cloud computing KW - cloud service KW - cloud storage KW - data security KW - privacy KW - confidentiality KW - acceptance process Y1 - 2016 U6 - https://doi.org/10.4338/ACI-2016-07-RA-0107 SN - 1869-0327 VL - 7 SP - 983 EP - 993 PB - Schattauer CY - Stuttgart ER - TY - JOUR A1 - Hacker, Philipp A1 - Naumann, Felix A1 - Friedrich, Tobias A1 - Grundmann, Stefan A1 - Lehmann, Anja A1 - Zech, Herbert T1 - AI compliance - challenges of bridging data science and law JF - Journal of Data and Information Quality (JDIQ) N2 - This vision article outlines the main building blocks of what we term AI Compliance, an effort to bridge two complementary research areas: computer science and the law. Such research has the goal to model, measure, and affect the quality of AI artifacts, such as data, models, and applications, to then facilitate adherence to legal standards. KW - AI Act KW - compliance KW - liability KW - privacy KW - transparency KW - information quality Y1 - 2022 U6 - https://doi.org/10.1145/3531532 SN - 1936-1955 SN - 1936-1963 VL - 14 IS - 3 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Möllers, Norma Tamaria A1 - Hälterlein, Jens T1 - Privacy issues in public discourse the case of "smart" CCTV in Germany JF - Innovation : the European journal of social sciences N2 - 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. KW - smart CCTV KW - video surveillance KW - privacy KW - data protection KW - sociology of knowledge KW - discourse analysis Y1 - 2013 U6 - https://doi.org/10.1080/13511610.2013.723396 SN - 1351-1610 VL - 26 IS - 1-2 SP - 57 EP - 70 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - XinYing, Chew A1 - Tiberius, Victor A1 - Alnoor, Alhamzah A1 - Camilleri, Mark A1 - Khaw, Khai Wah T1 - The dark side of metaverse: a multi-perspective of deviant behaviors from PLS-SEM and fsQCA findings JF - International journal of human–computer interaction N2 - The metaverse has created a huge buzz of interest because such a phenomenon is emerging. The behavioral aspect of the metaverse includes user engagement and deviant behaviors in the metaverse. Such technology has brought various dangers to individuals and society. There are growing cases reported of sexual abuse, racism, harassment, hate speech, and bullying because of online disinhibition make us feel more relaxed. This study responded to the literature call by investigating the effect of technical and social features through mediating roles of security and privacy on deviant behaviors in the metaverse. The data collected from virtual network users reached 1121 respondents. Partial Least Squares based structural equation modeling (PLS-SEM) and fuzzy set Qualitative Comparative Analysis (fsQCA) were used. PLS-SEM results revealed that social features such as user-to-user interaction, homophily, social ties, and social identity, and technical design such as immersive experience and invisibility significantly affect users’ deviant behavior in the metaverse. The fsQCA results provided insights into the multiple causal solutions and configurations. This study is exceptional because it provided decisive results by understanding the deviant behavior of users based on the symmetrical and asymmetrical approach to virtual networks. KW - deviant behaviors KW - metaverse KW - sociotechnical KW - perspective KW - privacy KW - fsQCA Y1 - 2024 U6 - https://doi.org/10.1080/10447318.2024.2331875 SN - 1044-7318 SN - 1532-7590 PB - Taylor & Francis CY - London ER -