TY - JOUR A1 - Belcheva, Veronika A1 - Ermakova, Tatiana A1 - Fabian, Benjamin T1 - Understanding Website Privacy Policies—A Longitudinal Analysis Using Natural Language Processing JF - Information N2 - Privacy policies are the main method for informing Internet users of how their data are collected and shared. This study aims to analyze the deficiencies of privacy policies in terms of readability, vague statements, and the use of pacifying phrases concerning privacy. This represents the undertaking of a step forward in the literature on this topic through a comprehensive analysis encompassing both time and website coverage. It characterizes trends across website categories, top-level domains, and popularity ranks. Furthermore, studying the development in the context of the General Data Protection Regulation (GDPR) offers insights into the impact of regulations on policy comprehensibility. The findings reveal a concerning trend: privacy policies have grown longer and more ambiguous, making it challenging for users to comprehend them. Notably, there is an increased proportion of vague statements, while clear statements have seen a decrease. Despite this, the study highlights a steady rise in the inclusion of reassuring statements aimed at alleviating readers’ privacy concerns. KW - privacy policy KW - longitudinal analysis KW - text analysis KW - NLP KW - readability KW - vagueness Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18268 SN - 2078-2489 VL - 14 IS - 11 PB - MDPI ER - TY - JOUR A1 - Dhiman, Rachit A1 - Miteff, Sofia A1 - Wang, Yuancheng A1 - Ma, Shih-Chi A1 - Amirikas, Ramila A1 - Fabian, Benjamin T1 - Artificial Intelligence and Sustainability—A Review JF - Analytics N2 - In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into “Sustainability of AI” and “AI for Sustainability”, revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field’s maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations’ Sustainable Development Goals (SDGs), and addressing stakeholders’ influence. KW - artificial intelligence KW - AI KW - sustainability KW - systematic mapping study Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18651 SN - 2813-2203 VL - 3 IS - 1 SP - 140 EP - 164 PB - MDPI ER - TY - JOUR A1 - Rosendorff, André A1 - Hodes, Alexander A1 - Fabian, Benjamin T1 - Artificial intelligence for last-mile logistics - Procedures and architecture JF - The Online Journal of Applied Knowledge Management (OJAKM) N2 - Artificial Intelligence (AI) is becoming increasingly important in many industries due to its diverse areas of application and potential. In logistics in particular, increasing customer demands and the growth in shipment volumes are leading to difficulties in forecasting delivery times, especially for the last mile. This paper explores the potential of using AI to improve delivery forecasting. For this purpose, a structured theoretical solution approach and a method for improving delivery forecasting using AI are presented. In doing so, the important phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, a standard process for data mining, are adopted and discussed in detail to illustrate the complexity and importance of each task such as data preparation or evaluation. Subsequently, by embedding the described solution into an overall system architecture for information systems, ideas for the integration of the solution into the complexity of real information systems for logistics are given. KW - supply chain management KW - logistics KW - artificial intelligence KW - machine learning KW - business intelligence Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15586 SN - 2325-4688 VL - 9 IS - 1 SP - 46 EP - 61 PB - The International Institute for Applied Knowledge Management ER - TY - CHAP A1 - Ververis, Vasilis A1 - Ermakova, Tatiana A1 - Isaakidis, Marios A1 - Basso, Simone A1 - Fabian, Benjamin A1 - Milan, Stefania T1 - Understanding Internet Censorship in Europe: The Case of Spain T2 - WebSci '21: Proceedings of the 13th ACM Web Science Conference N2 - European Union (EU) member states consider themselves bulwarks of democracy and freedom of speech. However, there is a lack of empirical studies assessing possible violations of these principles in the EU through Internet censorship. This work starts addressing this research gap by investigating Internet censorship in Spain over 2016-2020, including the controversial 2017 Catalan independence referendum. We focus, in particular, on network interference disrupting the regular operation of Internet services or contents. We analyzed the data collected by the Open Observatory of Network Interference (OONI) network measurement tool. The measurements targeted civil rights defending websites, secure communication tools, extremist political content, and information portals for the Catalan referendum. Our analysis indicates the existence of advanced network interference techniques that grow in sophistication over time. Internet Service Providers (ISPs) initially introduced information controls for a clearly defined legal scope (i.e., copyright infringement). Our research observed that such information controls had been re-purposed (e.g., to target websites supporting the referendum). We present evidence of network interference from all the major ISPs in Spain, serving 91% of mobile and 98% of broadband users and several governmental and law enforcement authorities. In these measurements, we detected 16 unique blockpages, 2 Deep Packet Inspection (DPI) vendors, and 78 blocked websites. We also contribute an enhanced domain testing methodology to detect certain kinds of Transport Layer Security (TLS) blocking that OONI could not initially detect. In light of our experience analyzing this dataset, we also make suggestions on improving the collection of evidence of network interference. Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-14105 SP - 319 EP - 328 ER - TY - JOUR A1 - Fernholz, Yannick A1 - Ermakova, Tatiana A1 - Fabian, Benjamin A1 - Buxmann, Peter T1 - User-driven prioritization of ethical principles for artificial intelligence systems JF - Computers in Human Behavior: Artificial Humans N2 - Despite the progress of Artificial Intelligence (AI) and its contribution to the advancement of human society, the prioritization of ethical principles from the viewpoint of its users has not yet received much attention and empirical investigations. This is important to develop appropriate safeguards and increase the acceptance of AI-mediated technologies among all members of society. In this research, we collected, integrated, and prioritized ethical principles for AI systems with respect to their relevance in different real-life application scenarios. First, an overview of ethical principles for AI was systematically derived from various academic and non-academic sources. Our results clearly show that transparency, justice and fairness, non-maleficence, responsibility, and privacy are most frequently mentioned in this corpus of documents. Next, an empirical survey to systematically identify users’ priorities was designed and conducted in the context of selected scenarios: AI-mediated recruitment (human resources), predictive policing, autonomous vehicles, and hospital robots. We anticipate that the resulting ranking can serve as a valuable basis for formulating requirements for AI-mediated solutions and creating AI algorithms that prioritize user’s needs. Our target audience includes everyone who will be affected by AI systems, e.g., policy makers, algorithm developers, and system managers as our ranking clearly depicts user’s awareness regarding AI ethics. KW - artificial intelligence KW - ethics KW - ethical guidelines KW - trustworthy AI KW - requirements prioritization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18509 SN - 2949-8821 VL - 2 IS - 1 PB - Elsevier ER - TY - CHAP A1 - Mehner, Caroline A1 - Fernholz, Yannick A1 - Fabian, Benjamin A1 - Ermakova, Tatiana ED - Auth, Gunnar ED - Pidun, Tim T1 - Predictive Policing – Eine kritische Bestandsaufnahme am Beispiel der Dimension Raum T2 - 6. Fachtagung Rechts- und Verwaltungsinformatik (RVI 2023) N2 - Dieser Beitrag bietet eine kritische Bestandsaufnahme des Predictive Policing am Beispiel der Dimension Raum. Unter Berücksichtigung der aktuellen Entwicklungen des europäischen AI-Acts werden Maßnahmen und Methoden beleuchtet und aus ethischer Perspektive reflektiert und diskutiert. Das methodische Fundament bildet eine systematische Literaturanalyse anhand einer Korpusanalyse zu Techniken des Predictive Policing. Es werden vorhandene wissenschaftliche Vorarbeiten vorgestellt und ethische Fragestellungen im Zusammenhang mit der Verwendung von Daten für Predictive Policing untersucht. Der Beitrag eröffnet wichtige Fragen, die es weiter zu erforschen gilt. Die aktuellen Entwicklungen im Rahmen des AI-Acts bestätigen die Relevanz der Thematik. KW - predictive policing KW - Raum KW - Künstliche Intelligenz KW - literature review KW - KI-Ethik Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18230 SN - 978-3-88579-735-7 N1 - 6. Fachtagung Rechts- und Verwaltungsinformatik (RVI 2023) SP - 55 EP - 67 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Ermakova, Tatiana A1 - Henke, Max A1 - Fabian, Benjamin T1 - Commercial Sentiment Analysis Solutions: A Comparative Study N2 - Empirical insights into high-promising commercial sentiment analysis solutions that go beyond their vendors’ claims are rare. Moreover, due to ongoing advances in the field, earlier studies are far from reflecting the current situation due to the constant evolution of the field. The present research aims to evaluate and compare current solutions. Based on tweets on the airline service quality, we test the solutions of six vendors with different market power, such as Amazon, Google, IBM, Microsoft, and Lexalytics, and MeaningCloud, and report their measures of accuracy, precision, recall, (macro) F1, time performance, and service level agreements (SLA). For positive and neutral classifications, none of the solutions showed precision of over 70%. For negative classifications, all of them demonstrate high precision of around 90%, however, only IBM Watson NLU and Google Cloud Natural Language achieve recall of over 70% and thus can be seen as worth considering for application scenarios w here negative text detection is a major concern. Overall, our study shows that an independent, critical experimental analysis of sentiment analysis services can provide interesting insights into their general reliability and particular classification accuracy beyond marketing claims to critically compare solutions based on real-world data and analyze potential weaknesses and margins of error before making an investment. KW - sentiment analysis KW - machine learning KW - text classification KW - commercial service KW - SaaS KW - cloud computing Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15509 SP - 103 EP - 114 PB - SciTePress ER - TY - JOUR A1 - Ververis, Vasilis A1 - Lasota, Lucas A1 - Ermakova, Tatiana A1 - Fabian, Benjamin T1 - Website blocking in the European Union: Network interference from the perspective of Open Internet JF - Policy & Internet N2 - By 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. KW - blocklist KW - DNS manipulation KW - EU KW - Internet censorship KW - network interference KW - Open Internet KW - nationalregulation authority KW - website blocking Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17982 SN - 1944-2866 VL - 16 IS - 1 SP - 121 EP - 148 PB - Wiley ER - TY - JOUR A1 - Ermakova, Tatiana A1 - Fabian, Benjamin A1 - Golimblevskaia, Elena A1 - Henke, Max T1 - A Comparison of Commercial Sentiment Analysis Services JF - SN Computer Science N2 - Empirical insights into promising commercial sentiment analysis solutions that go beyond the claims of their vendors are rare. Moreover, due to the constant evolution in the field, previous studies are far from reflecting the current situation. The goal of this article is to evaluate and compare current solutions using two experimental studies. In the first part of the study, based on tweets about airline service quality, we test the solutions of six vendors with different market power, such as Amazon, Google, IBM, Microsoft, Lexalytics, and MeaningCloud, and report their measures of accuracy, precision, recall, (macro)F1, time performance, and service level agreements (SLA). Furthermore, we compare two of the services in depth with multiple data sets and over time. The services tested here are Google Cloud Natural Language API and MeaningCloud Sentiment Analysis API. For evaluating the results over time, we use the same data set as in November 2020. In addition, further topic-specific and general Twitter data sets are used. The experiments show that the IBM Watson NLU and Google Cloud Natural Language API solutions may be preferred when negative text detection is the primary concern. When tested in July 2022, the Google Cloud Natural Language API was still the clear winner compared to the MeaningCloud Sentiment Analysis API, but only on the airline service quality data set; on the other data sets, both services provided specific benefits and drawbacks. Furthermore, we detected changes in the sentiment classification over time with both services. Our results motivate that an independent, critical, and longitudinal experimental analysis of sentiment analysis services can provide interesting insights into their overall reliability and particular classification accuracy beyond marketing claims to critically compare solutions based on real data and analyze potential weaknesses and margins of error before making an investment. KW - sentiment analysis KW - machine learning KW - text classification KW - commercial service KW - SaaS KW - cloud computing Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17598 SN - 2661-8907 VL - 4 PB - Springer Nature ER - TY - JOUR A1 - Langenberg, Anna A1 - Ma, Shih-Chi A1 - Ermakova, Tatiana A1 - Fabian, Benjamin T1 - Formal Group Fairness and Accuracy in Automated Decision Making JF - Mathematics N2 - Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learning are studied to experimentally analyze the relationship between accuracy and group fairness. The results indicated that group fairness and accuracy may even benefit each other, which emphasizes the importance of selecting appropriate measures for performance evaluation. This work provides a foundation for further studies on the adequate objectives of Machine Learning in the context of fair automated decision making. KW - AI KW - machine learning KW - automated decision making KW - algorithmic bias KW - metric KW - group fairness Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17323 SN - 2227-7390 VL - 11 IS - 8 PB - MDPI ER - TY - JOUR A1 - Oehlers, Milena A1 - Fabian, Benjamin T1 - Graph Metrics for Network Robustness—A Survey JF - Mathematics N2 - Research on the robustness of networks, and in particular the Internet, has gained critical importance in recent decades because more and more individuals, societies and firms rely on this global network infrastructure for communication, knowledge transfer, business processes and e-commerce. In particular, modeling the structure of the Internet has inspired several novel graph metrics for assessing important topological robustness features of large complex networks. This survey provides a comparative overview of these metrics, presents their strengths and limitations for analyzing the robustness of the Internet topology, and outlines a conceptual tool set in order to facilitate their future adoption by Internet research and practice but also other areas of network science. KW - network science KW - Internet KW - graph KW - robustness KW - metric Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-13954 SN - 2227-7390 VL - 9 IS - 8 PB - MDPI ER -