Quality Characteristics of a Software Platform for Human-AI Teaming in Smart Manufacturing

TL;DR

This paper investigates the key quality characteristics for a human-AI teaming software platform in smart manufacturing, emphasizing trustworthiness, reliability, and security, based on stakeholder interviews and discussing metrics for assessment.

Contribution

It identifies and prioritizes relevant quality characteristics for human-AI collaboration in smart manufacturing through stakeholder insights and proposes metrics for their evaluation.

Findings

Trustworthiness, functional suitability, reliability, and security are most important.

Relevance of quality characteristics varies with use case.

Stakeholders prioritize improving production, efficiency, and safety.

Abstract

As AI-enabled software systems become more prevalent in smart manufacturing, their role shifts from a reactive to a proactive one that provides context-specific support to machine operators. In the context of an international research project, we develop an AI-based software platform that shall facilitate the collaboration between human operators and manufacturing machines. We conducted 14 structured interviews with stakeholders of the prospective software platform in order to determine the individual relevance of selected quality characteristics for human-AI teaming in smart manufacturing. These characteristics include the ISO 25010:2011 standard for software quality and AI-specific quality characteristics such as trustworthiness, explicability, and auditability. The interviewees rated trustworthiness, functional suitability, reliability, and security as the most important quality characteristics for this context, and portability, compatibility, and maintainability as the least important. Also, we observed agreement regarding the relevance of the quality characteristics among interviewees having the same role. On the other hand, the relevance of each quality characteristics varied depending on the concrete use case of the prospective software platform. The interviewees also were asked about the key success factors related to human-AI teaming in smart manufacturing. They identified improving the production cycle, increasing operator efficiency, reducing scrap, and reducing ergonomic risks as key success criteria. In this paper, we also discuss metrics for measuring the fulfillment of these quality characteristics, which we intend to operationalize and monitor during operation of the prospective software platform.