arXiv subjects

Leona Lassak

Publications and source records attributed to Leona Lassak.

3 recordsLinked to original sources

arXiv2026

Exploring Usability and Legal Practice: Insights from German Judicial Users of Digital Forensics

Digital forensics has become an integral part of modern criminal proceedings, yet its effective integration remains challenging because of increasing data volumes, evolving technologies, and complex interactions between technical and legal stakeholders. Although prior work has focused primarily on digital forensic tools and methods, its broader procedural and organizational context has received limited attention. Building on emerging perspectives inspired by usability research and human-centered security, we conceptualize digital forensics as part of a socio-technical system within criminal proceedings. We consequently investigate this perspective through a survey of 101 practitioners from the judiciary of the German federal state of North Rhine-Westphalia, including public prosecutors, judges, and digital forensic experts. The results indicate a strong demand for improved integration of digital forensics into workflows, enhanced cross-domain communication, and a closer alignment of stakeholder expectations. They also uncover great potential for the improvement of digital forensics usability, e.g., through stronger interdisciplinary cooperation, easier and faster access to evidential data and results, or improvement of stakeholder training and education.

cs.CR↗

arXiv2025

From Assistance to Autonomy -- A Researcher Study on the Potential of AI Support for Qualitative Data Analysis

The advent of AI technologies, such as Large Language Models, has introduced new possibilities for Qualitative Data Analysis (QDA), offering both opportunities and challenges. To help navigate the responsible integration of AI into QDA, we conducted semi-structured interviews with 15 Human-Computer Interaction (HCI) researchers experienced in QDA. While our participants were open to AI support in their QDA workflows, they expressed concerns about data privacy, autonomy, and the quality of AI outputs. In response, we developed a framework that spans from minimal to high AI involvement, providing tangible scenarios for integrating AI into QDA practices while addressing researchers' needs and concerns. Aligned with real-life QDA workflows, we identify potential for AI tools in areas such as data pre-processing, researcher onboarding, or conflict mediation. Our framework aims to provoke further discussion on the development of AI-supported QDA and to help establish community standards for responsible Human-AI collaboration.

cs.CY↗

arXiv2024

Understanding Users' Interaction with Login Notifications

Login notifications intend to inform users about sign-ins and help them protect their accounts from unauthorized access. Notifications are usually sent if a login deviates from previous ones, potentially indicating malicious activity. They contain information like the location, date, time, and device used to sign in. Users are challenged to verify whether they recognize the login (because it was them or someone they know) or to protect their account from unwanted access. In a user study, we explore users' comprehension, reactions, and expectations of login notifications. We utilize two treatments to measure users' behavior in response to notifications sent for a login they initiated or based on a malicious actor relying on statistical sign-in information. We find that users identify legitimate logins but need more support to halt malicious sign-ins. We discuss the identified problems and give recommendations for service providers to ensure usable and secure logins for everyone.

cs.HC↗