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  <doc>
    <id>1550</id>
    <completedYear>2021</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>103</pageFirst>
    <pageLast>114</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>SciTePress</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Commercial Sentiment Analysis Solutions: A Comparative Study</title>
    <abstract language="eng">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.</abstract>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-15509</identifier>
    <enrichment key="opus.import.date">2021-11-01T09:24:50+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="opus.import.file">filename=phpicjqnE</enrichment>
    <enrichment key="opus.import.checksum">937778ce300d1920bcebf6b9b3cbf88a</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.5220/0010709400003058</enrichment>
    <enrichment key="SourceTitle">Ermakova, T.; Henke, M. and Fabian, B. (2021). Commercial Sentiment Analysis Solutions: A Comparative Study. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST, ISBN 978-989-758-536-4, pages 103-114. DOI: 10.5220/0010709400003058</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Tatiana Ermakova</author>
    <author>Max Henke</author>
    <author>Benjamin Fabian</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sentiment analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>text classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>commercial service</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SaaS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cloud computing</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft, Informatik, Recht</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1550/Commercial_Sentiment_Analysis_Solutions.pdf</file>
  </doc>
  <doc>
    <id>1732</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>11</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Formal Group Fairness and Accuracy in Automated Decision Making</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Mathematics</parentTitle>
    <identifier type="issn">2227-7390</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-17323</identifier>
    <enrichment key="opus.import.data">@Articlemath11081771, AUTHOR = Langenberg, Anna and Ma, Shih-Chi and Ermakova, Tatiana and Fabian, Benjamin, TITLE = Formal Group Fairness and Accuracy in Automated Decision Making, JOURNAL = Mathematics, VOLUME = 11, YEAR = 2023, NUMBER = 8, ARTICLE-NUMBER = 1771, URL = https://www.mdpi.com/2227-7390/11/8/1771, ISSN = 2227-7390, ABSTRACT = 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., DOI = 10.3390/math11081771</enrichment>
    <enrichment key="opus.import.dataHash">md5:ec9484250a12bf75e259960d91ea89a2</enrichment>
    <enrichment key="opus.import.date">2023-04-17T11:21:25+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpUphNAd</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">643d2bb52cf480.33016704</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.3390/math11081771</enrichment>
    <enrichment key="SourceTitle">Langenberg, A.; Ma, S.-C.; Ermakova, T.; Fabian, B. Formal Group Fairness and Accuracy in Automated Decision Making. Mathematics 2023, 11, 1771. https://doi.org/10.3390/math11081771</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Anna Langenberg</author>
    <author>Shih-Chi Ma</author>
    <author>Tatiana Ermakova</author>
    <author>Benjamin Fabian</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated decision making</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>algorithmic bias</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>metric</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>group fairness</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="519">Wahrscheinlichkeiten, angewandte Mathematik</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft, Informatik, Recht</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1732/mathematics-11-01771.pdf</file>
  </doc>
  <doc>
    <id>1798</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>121</pageFirst>
    <pageLast>148</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>16</volume>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Website blocking in the European Union: Network interference from the perspective of Open Internet</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Policy &amp; Internet</parentTitle>
    <identifier type="issn">1944-2866</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-17982</identifier>
    <enrichment key="opus.import.date">2023-09-21T08:54:02+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.1002/poi3.367</enrichment>
    <enrichment key="SourceTitle">Ververis, V., Lasota, L., Ermakova, T., &amp; Fabian, B. (2024). Website blocking in the European Union: Network interference from the perspective of Open Internet. Policy &amp; Internet, 16, 121–148. https://doi.org/10.1002/poi3.367</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Vasilis Ververis</author>
    <author>Lucas Lasota</author>
    <author>Tatiana Ermakova</author>
    <author>Benjamin Fabian</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>blocklist</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DNS manipulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>EU</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Internet censorship</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>network interference</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Open Internet</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nationalregulation authority</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>website blocking</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="ddc" number="320">Politikwissenschaft</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft, Informatik, Recht</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="4">Hybrid Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1798/1798.pdf</file>
  </doc>
  <doc>
    <id>1850</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>2</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">User-driven prioritization of ethical principles for artificial intelligence systems</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Computers in Human Behavior: Artificial Humans</parentTitle>
    <identifier type="issn">2949-8821</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-18509</identifier>
    <enrichment key="opus.import.date">2024-02-12T09:10:59+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.1016/j.chbah.2024.100055</enrichment>
    <enrichment key="SourceTitle">Fernholz, Y., Ermakova, T., Fabian, B., &amp; Buxmann, P. (2024). User-driven prioritization of ethical principles for artificial intelligence systems. Computers in Human Behavior: Artificial Humans, 2(1), 100055. doi:10.1016/j.chbah.2024.100055</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Yannick Fernholz</author>
    <author>Tatiana Ermakova</author>
    <author>Benjamin Fabian</author>
    <author>Peter Buxmann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ethics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ethical guidelines</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>trustworthy AI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>requirements prioritization</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="170">Ethik</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft, Informatik, Recht</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1850/1-s2.0-S294988212400015X-main.pdf</file>
  </doc>
  <doc>
    <id>1759</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>4</volume>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Comparison of Commercial Sentiment Analysis Services</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">SN Computer Science</parentTitle>
    <identifier type="issn">2661-8907</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-17598</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.1007/s42979-023-01886-y</enrichment>
    <enrichment key="SourceTitle">Ermakova, T., Fabian, B., Golimblevskaia, E. et al. A Comparison of Commercial Sentiment Analysis Services. SN COMPUT. SCI. 4, 477 (2023). https://doi.org/10.1007/s42979-023-01886-y</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Tatiana Ermakova</author>
    <author>Benjamin Fabian</author>
    <author>Elena Golimblevskaia</author>
    <author>Max Henke</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sentiment analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>text classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>commercial service</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SaaS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cloud computing</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fachbereich Wirtschaft, Informatik, Recht</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Funding" number="">Projekt DEAL</collection>
    <collection role="green_open_access" number="4">Hybrid Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1759/s42979-023-01886-y.pdf</file>
  </doc>
  <doc>
    <id>1823</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>55</pageFirst>
    <pageLast>67</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Gesellschaft für Informatik e.V.</publisherName>
    <publisherPlace>Bonn</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Predictive Policing – Eine kritische Bestandsaufnahme am Beispiel der Dimension Raum</title>
    <abstract language="deu">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.</abstract>
    <parentTitle language="deu">6. Fachtagung Rechts- und Verwaltungsinformatik (RVI 2023)</parentTitle>
    <identifier type="isbn">978-3-88579-735-7</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-18230</identifier>
    <note>6. Fachtagung Rechts- und Verwaltungsinformatik (RVI 2023)</note>
    <enrichment key="opus.import.date">2023-11-14T09:27:58+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.18420/rvi2023-015</enrichment>
    <enrichment key="SourceTitle">Mehner, Caroline; Fernholz, Yannick; Fabian, Benjamin; Ermakova, Tatiana (2023): Predictive Policing – Eine kritische Bestandsaufnahme am Beispiel der Dimension Raum. 6. Fachtagung Rechts- und Verwaltungsinformatik (RVI 2023). DOI: 10.18420/rvi2023-015. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-735-7. pp. 55-67. Regular Research Papers. Dresden. 26.-27. October 2023</enrichment>
    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Caroline Mehner</author>
    <author>Yannick Fernholz</author>
    <author>Benjamin Fabian</author>
    <author>Tatiana Ermakova</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>predictive policing</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Raum</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>literature review</value>
    </subject>
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      <value>KI-Ethik</value>
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    <pageFirst>319</pageFirst>
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    <title language="eng">Understanding Internet Censorship in Europe: The Case of Spain</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="deu">WebSci '21: Proceedings of the 13th ACM Web Science Conference</parentTitle>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-14105</identifier>
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    <enrichment key="SourceTitle">Vasilis Ververis, Tatiana Ermakova, Marios Isaakidis, Simone Basso, Benjamin Fabian, and Stefania Milan. 2021. Understanding Internet Censorship in Europe: The Case of Spain. In 13th ACM Web Science Conference 2021 (WebSci '21). Association for Computing Machinery, New York, NY, USA, 319–328. DOI:https://doi.org/10.1145/3447535.3462638</enrichment>
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    <author>Vasilis Ververis</author>
    <author>Tatiana Ermakova</author>
    <author>Marios Isaakidis</author>
    <author>Simone Basso</author>
    <author>Benjamin Fabian</author>
    <author>Stefania Milan</author>
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    <issue>11</issue>
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    <title language="eng">Understanding Website Privacy Policies—A Longitudinal Analysis Using Natural Language Processing</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Information</parentTitle>
    <identifier type="issn">2078-2489</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-18268</identifier>
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    <enrichment key="SourceTitle">Belcheva, V.; Ermakova, T.; Fabian, B. Understanding Website Privacy Policies—A Longitudinal Analysis Using Natural Language Processing. Information 2023, 14, 622. https://doi.org/10.3390/info14110622</enrichment>
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    <author>Veronika Belcheva</author>
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      <type>uncontrolled</type>
      <value>readability</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vagueness</value>
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