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Rita Gsenger

Publications and source records attributed to Rita Gsenger.

4 recordsLinked to original sources

Doing Audits Right? The Role of Sampling and Legal Content Analysis in Systemic Risk Assessments and Independent Audits in the Digital Services Act

A central requirement of the European Union's Digital Services Act (DSA) is that online platforms undergo internal and external audits. A key component of these audits is the assessment of systemic risks, including the dissemination of illegal content, threats to fundamental rights, impacts on democratic processes, and gender-based violence. The DSA Delegated Regulation outlines how such audits should be conducted, setting expectations for both platforms and auditors. This article evaluates the strengths and limitations of different qualitative and quantitative methods for auditing these systemic risks and proposes a mixed-method approach for DSA compliance. We argue that content sampling, combined with legal and empirical analysis, offers a viable method for risk-specific audits. First, we examine relevant legal provisions on sample selection for audit purposes. We then assess sampling techniques and methods suitable for detecting systemic risks, focusing on how representativeness can be understood across disciplines. Finally, we review initial systemic risk assessment reports submitted by platforms, analyzing their testing and sampling methodologies. By proposing a structured, mixed-method approach tailored to specific risk categories and platform characteristics, this article addresses the challenge of evidence-based audits under the DSA. Our contribution emphasizes the need for adaptable, context-sensitive auditing strategies and adds to the emerging field of DSA compliance research.

cs.CY

Mapping Compliance: A Taxonomy for Political Content Analysis under the EU's Digital Electoral Framework

The rise of digital platforms has transformed political campaigning, introducing complex regulatory challenges. This paper presents a comprehensive taxonomy for analyzing political content in the EU's digital electoral landscape, aligning with the requirements set forth in new regulations, such as the Digital Services Act. Using a legal doctrinal methodology, we construct a detailed codebook that enables systematic content analysis across user-generated and political ad content to assess compliance with regulatory mandates.

cs.CY

Wer ist schuld, wenn Algorithmen irren? Entscheidungsautomatisierung, Organisationen und Verantwortung

Algorithmic decision support (ADS) is increasingly used in a whole array of different contexts and structures in various areas of society, influencing many people's lives. Its use raises questions, among others, about accountability, transparency and responsibility. Our article aims to give a brief overview of the central issues connected to ADS, responsibility and decision-making in organisational contexts and identify open questions and research gaps. Furthermore, we describe a set of guidelines and a complementary digital tool to assist practitioners in mapping responsibility when introducing ADS within their organisational context. -- Algorithmenunterstützte Entscheidungsfindung (algorithmic decision support, ADS) kommt in verschiedenen Kontexten und Strukturen vermehrt zum Einsatz und beeinflusst in diversen gesellschaftlichen Bereichen das Leben vieler Menschen. Ihr Einsatz wirft einige Fragen auf, unter anderem zu den Themen Rechenschaft, Transparenz und Verantwortung. Im Folgenden möchten wir einen Überblick über die wichtigsten Fragestellungen rund um ADS, Verantwortung und Entscheidungsfindung in organisationalen Kontexten geben und einige offene Fragen und Forschungslücken aufzeigen. Weiters beschreiben wir als konkrete Hilfestellung für die Praxis einen von uns entwickelten Leitfaden samt ergänzendem digitalem Tool, welches Anwender:innen insbesondere bei der Verortung und Zuordnung von Verantwortung bei der Nutzung von ADS in organisationalen Kontexten helfen soll.

cs.CY

"Computer Says No": Algorithmic Decision Support and Organisational Responsibility

Algorithmic decision support is increasingly used in a whole array of different contexts and structures in various areas of society, influencing many people's lives. Its use raises questions, among others, about accountability, transparency and responsibility. While there is substantial research on the issue of algorithmic systems and responsibility in general, there is little to no prior research on organisational responsibility and its attribution. Our article aims to fill that gap; we give a brief overview of the central issues connected to ADS, responsibility and decision-making in organisational contexts and identify open questions and research gaps. Furthermore, we describe a set of guidelines and a complementary digital tool to assist practitioners in mapping responsibility when introducing ADS within their organisational context.

cs.CY