3,387 research outputs found
Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work
The increasing prevalence of Artificial Intelligence (AI) in safety-critical
contexts such as air-traffic control leads to systems that are practical and
efficient, and to some extent explainable to humans to be trusted and accepted.
The present structured literature analysis examines n = 236 articles on the
requirements for the explainability and acceptance of AI. Results include a
comprehensive review of n = 48 articles on information people need to perceive
an AI as explainable, the information needed to accept an AI, and
representation and interaction methods promoting trust in an AI. Results
indicate that the two main groups of users are developers who require
information about the internal operations of the model and end users who
require information about AI results or behavior. Users' information needs vary
in specificity, complexity, and urgency and must consider context, domain
knowledge, and the user's cognitive resources. The acceptance of AI systems
depends on information about the system's functions and performance, privacy
and ethical considerations, as well as goal-supporting information tailored to
individual preferences and information to establish trust in the system.
Information about the system's limitations and potential failures can increase
acceptance and trust. Trusted interaction methods are human-like, including
natural language, speech, text, and visual representations such as graphs,
charts, and animations. Our results have significant implications for future
human-centric AI systems being developed. Thus, they are suitable as input for
further application-specific investigations of user needs
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
On the Multiple Roles of Ontologies in Explainable AI
This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness
On the Multiple Roles of Ontologies in Explainable AI
This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness
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