Designing for Bi-Directional Transparency in Human-AI-Robot-Teaming

Abstract

This paper takes a practitioner’s perspective on advancing bi-directional transparency in human-AI-robot teams (HARTs). Bi-directional transparency is important for HARTs because the better that people and artificially intelligent agents can understand one another’s capabilities, limits, inputs, outputs and contexts in a given task environment; the better they can work as a team to accomplish shared goals, interdependent tasks, and overall missions. This understanding can be built, augmented, broken and repaired at various stages across the technology life cycle, including the conceptual design; iterative design of software, hardware and interfaces; marketing and sales; system training; operational use; and system updating and adaptation stages. This paper provides an overview of some best practices and challenges in building this bi-directional transparency at different points in the technology life cycle of human-AI-robot systems. The goal is to help advance a wider discussion and sharing of lessons learned from recent work in this area.