Requirements Elicitation and Stakeholder Communications for Explainable Machine Learning Systems: State of the Art

Machine Learning (ML) systems are currently used widely. While the requirements elicitation and management processes are well understood and standardized for software systems, they have not been adopted for ML systems. These processes pose additional complexity due to the multitude of stakeholders,...

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Bibliographic Details
Main Authors: Das, Saikat, Hossain, Mazharul, Shiva, Sajjan G.
Format: Conference Proceeding
Language:English
Subjects:
Closed box
Cognition
Complexity theory
Explainability
Machine learning
Requirement management
Requirements Elicitation
Requirements management
Software systems
Stakeholder communications
Stakeholders
Online Access:Request full text
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Summary:Machine Learning (ML) systems are currently used widely. While the requirements elicitation and management processes are well understood and standardized for software systems, they have not been adopted for ML systems. These processes pose additional complexity due to the multitude of stakeholders, their understanding of ML concepts, and their varied requirement orientations. Communication among the stakeholders is a must to generate an appropriate requirements document to guide the development of the system. Another aspect of ML systems is their black box mode of operation. It is essential to know the reasoning behind the predictions of these systems, especially for critical applications. These explainability requirements also must be taken into consideration. This paper reviews state of the art in these two areas to form a basis for developing a framework for stakeholder interaction to facilitate the requirement management of ML systems.
ISSN:2831-3399
DOI:10.1109/ICIT58056.2023.10225934