Ontology-Based Modeling and Analysis of Trustworthiness Requirements: Preliminary Results

Glenda Amaral, Renata Guizzardi, Giancarlo Guizzardi, John Mylopoulos

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

20 Citations (Scopus)

Abstract

The advent of Artificial Intelligence (AI) technologies has made it possible to build systems that diagnose a patient, decide on a loan application, drive a car, or kill an adversary in combat. Such systems signal a new era where software-intensive systems perform tasks that were performed in the past only by humans because they require judgement that only humans possess. However, such systems need to be trusted by their users, in the same way that a lawyer, medical doctor, driver or soldier is trusted in performing the tasks she is trained for. This creates the need for a new class of requirements, Trustworthiness Requirements, that we have to study in order to develop techniques for their elicitation, analysis and operationalization. In this paper, we propose a foundation to develop such techniques. Our work is based on an Ontology of Trust that answers questions about the nature of trust and the factors that influence it. Based on the answers, we characterize the class of trustworthiness requirements. Among other things, this characterization supports the requirements engineer in defining thurstworthiness requirements, identifying the risks presented by the system-to-be, and understanding the signals the system must emit to gain and maintain trust.

Original languageEnglish
Title of host publicationConceptual Modeling
Subtitle of host publication39th International Conference, ER 2020, Vienna, Austria, November 3–6, 2020, Proceedings
EditorsGillian Dobbie, Ulrich Frank, Gerti Kappel, Stephen W. Liddle, Heinrich C. Mayr
PublisherSpringer
Pages342-352
Number of pages11
Volume12400
ISBN (Electronic)978-3-030-62522-1
ISBN (Print)978-3-030-62521-4
DOIs
Publication statusPublished - 29 Oct 2020
Externally publishedYes
Event39th International Conference on Conceptual Modeling, ER 2020 - Virtual Event
Duration: 3 Nov 20206 Nov 2020
Conference number: 39
https://er2020.big.tuwien.ac.at/

Publication series

NameLecture Notes in Computer Science
Volume12400

Conference

Conference39th International Conference on Conceptual Modeling, ER 2020
Abbreviated titleER 2020
Period3/11/206/11/20
Internet address

Keywords

  • Trustworthiness requirements
  • AI systems
  • OntoUML
  • n/a OA procedure

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