What's up with Requirements Engineering for Artificial Intelligence Systems?

In traditional approaches to building software systems (that do not include an Artificial Intelligent (AI) or Machine Learning (ML) component), Requirements Engineering (RE) activities are well-established and researched. However, building software systems with one or more AI components may depend heavily on data with limited or no insight into the system's workings. Therefore, engineering such systems poses significant new challenges to RE. Our search showed that literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). Our study's main objective was to investigate current approaches in writing requirements for AI/ML systems, identify available tools and techniques used to model requirements, and find existing challenges and limitations. We performed a Systematic Literature Review (SLR) of current RE4AI methods and identified 27 primary studies. Using these studies, we analysed the key tools and techniques used to specify and model requirements and found several challenges and limitations of existing RE4AI practices. We further provide recommendations for future research, based on our analysis of the primary studies and mapping to industry guidelines in Google PAIR). The SLR findings highlighted that present RE applications were not adaptive to manage most AUML systems and emphasised the need to provide new techniques and tools to support RE4AI.