Agile System Development Lifecycle for AI Systems: Decision Architecture
Asif Q. Gill

TL;DR
This paper proposes integrating decision science into agile SDLC to better support AI systems focused on decision automation, demonstrated through an insurance claim processing example.
Contribution
It introduces a novel approach combining decision science with agile SDLC specifically for AI decision-automation systems.
Findings
Decision science enhances AI system development.
The approach is applicable to insurance claim processing.
Initial usability of decision architecture in agile SDLC.
Abstract
Agile system development life cycle (SDLC) focuses on typical functional and non-functional system requirements for developing traditional software systems. However, Artificial Intelligent (AI) systems are different in nature and have distinct attributes such as (1) autonomy, (2) adaptiveness, (3) content generation, (4) decision-making, (5) predictability and (6) recommendation. Agile SDLC needs to be enhanced to support the AI system development and ongoing post-deployment adaptation. The challenge is: how can agile SDLC be enhanced to support AI systems? The scope of this paper is limited to AI system enabled decision automation. Thus, this paper proposes the use of decision science to enhance the agile SDLC to support the AI system development. Decision science is the study of decision-making, which seems useful to identify, analyse and describe decisions and their architecture…
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Taxonomy
TopicsBig Data and Business Intelligence · Business Process Modeling and Analysis · Software System Performance and Reliability
