Making sense of AI systems development

TL;DR

This paper examines how teams developing AI systems using IBM Watson face unexpected challenges due to difficulties in understanding the technology, data, and context, highlighting the importance of sensemaking in AI development.

Contribution

It provides an empirical analysis of sensemaking episodes in real-world AI projects, revealing challenges related to AI's inherent characteristics and the need for mindful development practices.

Findings

Teams struggle to establish reliable meanings about AI technology and data.

AI's dependency on large data sets complicates development.

Unanticipated sensemaking issues impact project success.

Abstract

We identify and describe episodes of sensemaking around challenges in modern AI-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI's inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.