Digitale Infrastrukturen in der Demokratie
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- ItemEinsatz von KI in der Medizin: Haftung und Versicherung(2023) Zech, Herbert; Hünefeld, Isabelle Céline
- ItemBeware: Processing of Personal Data—Informed Consent Through Risk Communication(2024) Seiling, Lukas; Gsenger, Rita; Mulugeta, Filmona; Henningsen, Marte; Mischau, Lena; Schirmbeck, MarieThe General Data Protection Regulation (GDPR) has been applicable since May 2018 and aims to further harmonize data protection law in the European Union. Processing personal data based on individuals’ consent is lawful under the GDPR only if such consent meets certain requirements and is “informed,” in particular. However, complex privacy notice design and individual cognitive limitations challenge data subjects’ ability to make elaborate consent decisions. Risk-based communication may address these issues. Literature review: Most research focuses on isolated aspects of risk in processing personal data, such as the actors involved, specific events leading to risk formation, or distinctive (context-dependent) consequences. We propose a model combining these approaches as the basis for context-independent risk communication. Research questions: 1. What are relevant information categories for risk communication in the processing of personal data online? 2. Which potentially adverse consequences can arise from specific events in the processing of personal data online? 3. How can consequences in the processing of personal data be avoided or mitigated? Research methodology: The GDPR was examined through a systematic qualitative content analysis. The results inform the analysis of 32 interviews with privacy, data protection, and information security experts from academia, Non-Governmental Organizations, the public, and the private sector. Results: Risk-relevant information categories, specific consequences, and relations between them are identified, along with strategies for risk mitigation. The study concludes with a specified framework for perceived risk in processing personal data. Conclusion: The results provide controllers, regulatory bodies, data subjects, and experts in the field of professional communication with information on risk formation in personal data processing. Based on our analysis, we propose information categories for risk communication, which expand the current regulatory information requirements.
- ItemWebsite blocking in the European Union: Network interference from the perspective of Open Internet(2024) Ververis, Vasilis; Lasota, Lucas; Ermakova, Tatiana; Fabian, BenjaminBy establishing an infrastructure for monitoring and blocking networks in accordance with European Union (EU) law on preventive measures against the spread of information, EU member states have also made it easier to block websites and services and monitor information. While relevant studies have documented Internet censorship in non‐European countries, as well as the use of such infrastructures for political reasons, this study examines network interference practices such as website blocking against the backdrop of an almost complete lack of EU‐related research. Specifically, it performs and demonstrates an analysis for the total of 27 EU countries based on three different sources. They include first, tens of millions of historical network measurements collected in 2020 by Open Observatory of Network Interference volunteers from around the world; second, the publicly available blocking lists used by EU member states; and third, the reports issued by network regulators in each country from May 2020 to April 2021. Our results show that authorities issue multiple types of blocklists. Internet Service Providers limit access to different types and categories of websites and services. Such resources are sometimes blocked for unknown reasons and not included in any of the publicly available blocklists. The study concludes with the hurdles related to network measurements and the nontransparency from regulators regarding specifying website addresses in blocking activities.
- ItemPerformance of the flood warning system in Germany in July 2021 – insights from affected residents(2023) Thieken, Annegret H.; Bubeck, Philip; Heidenreich, Anna; Von Keyserlingk, Jennifer; Dillenardt, Lisa; Otto, AntjeAbstract. In July 2021 intense rainfall caused devastating floods in western Europe and 184 fatalities in the German federal states of North Rhine-Westphalia (NW) and Rhineland-Palatinate (RP), calling into question their flood forecasting, warning and response system (FFWRS). Data from an online survey (n=1315) reveal that 35 % of the respondents from NW and 29 % from RP did not receive any warning. Of those who were warned, 85 % did not expect very severe flooding and 46 % reported a lack of situational knowledge on protective behaviour. Regression analysis reveals that this knowledge is influenced not only by gender and flood experience but also by the content and the source of the warning message. The results are complemented by analyses of media reports and official warnings that show shortcomings in providing adequate recommendations to people at risk. Still, the share of people who did not report any emergency response is low and comparable to other flood events. However, the perceived effectiveness of the protective behaviour was low and mainly compromised by high water levels and the perceived level of surprise about the flood magnitude. Good situational knowledge and a higher number of previously experienced floods were linked to performing more effective loss-reducing action. Dissemination of warnings, clearer communication of the expected flood magnitude and recommendations on adequate responses to a severe flood, particularly with regard to flash and pluvial floods, are seen as major entry points for improving the FFWRS in Germany.
- ItemProcess model forecasting and change exploration using time series analysis of event sequence data(2023) De Smedt, Johannes; Yeshchenko, Anton; Polyvyanyy, Artem; De Weerdt, Jochen; Mendling, JanProcess analytics is a collection of data-driven techniques for, among others, making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling analytical tasks such as next activity, remaining time, or outcome prediction. However, there is a notable void regarding predictions at the process model level. It is the ambition of this article to fill this gap. More specifically, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable description of the overall process for a given period in the future. Such a forecast helps, for instance, to anticipate and prepare for the consequences of upcoming process drifts and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding time series forecasting techniques can be applied. Our implementation demonstrates the feasibility of process model forecasting using real-world event data. A user study using our Process Change Exploration tool confirms the usefulness and ease of use of the produced process model forecasts.