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  4. Judging Alexa - Towards a New Methodology to Capture the Legal Compatibility of Conversational Speech Agents
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Judging Alexa - Towards a New Methodology to Capture the Legal Compatibility of Conversational Speech Agents

Journal
Conference on Conversational User Interfaces (CUI)
Type
conference paper
Date Issued
2020
Author(s)
Dickhaut, Ernestine
;
Thies, Laura Friederike
;
Janson, Andreas  
;
Roßnagel, Alexander
;
Leimeister, Jan Marco  
DOI
10.1145/3405755.3406160
Research Team
IWI6
Abstract
Higher legal standards with regards to data protection of individuals such as EU-GDPR are increasing the pressure on developers of IT artifacts. This is especially prevalent when considering conversational speech agents (CSA) that are collecting data in new ways, and thus, are oftentimes producing conflicts with existing law regulations. For this purpose, we introduce the law simulation method which is a well-known evaluation method among law researchers for capturing legal compatibility of IT artifacts such as CSA. With this rigorous method, we are able to derive actionable guidance for CSA developers to evaluate developer efforts for increasing legal compatibility. To illustrate our methodological approach, we describe in this workshop paper key steps of the method with respect to the evaluation of CSA. We discuss how this can serve as the foundation for a new evaluation method of legally compatible systems in information systems.
Language
English
Keywords
Conversational speech agent
Evaluation method
Legal compatibility
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Event Title
Conference on Conversational User Interfaces (CUI)
Event Location
Bilbao, Spain
Event Date
July, 2020
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/112645
Subject(s)

law

other research area

computer science

Division(s)

IWI - Institute of In...

Eprints ID
259763
File(s)
Thumbnail Image

open.access

Name

JML_778.pdf

Size

404.32 KB

Format

Adobe PDF

Checksum (MD5)

b03032c080f4c2f310a74f356a246956

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