Requirements practices and gaps when engineering human-centered Artificial Intelligence systems

Context: Engineering Artificial Intelligence (AI) software is a relatively new area with many challenges, unknowns, and limited proven best practices. Big companies such as Google, Microsoft, and Apple have provided a suite of recent guidelines to assist engineering teams in building human-centered AI systems. Objective: The practices currently adopted by practitioners for developing such systems, especially during Requirements Engineering (RE), are little studied and reported to date. Method: This paper presents the results of a survey conducted to understand current industry practices in RE for AI (RE4AI) and to determine which key human-centered AI guidelines should be followed. Our survey is based on mapping existing industrial guidelines, best practices, and efforts in the literature. Results: We surveyed 29 professionals and found most participants agreed that all the human-centered aspects we mapped should be addressed in RE. Further, we found that most participants were using UML or Microsoft Office to present requirements. Conclusion: We identify that most of the tools currently used are not equipped to manage AI-based software, and the use of UML and Office may pose issues with the quality of requirements captured for AI. Also, all human-centered practices mapped from the guidelines should be included in RE.