Operationalizing Meaningful Human Control for Human-Robot Interface Design

Autonomous robots are increasingly deployed in sensitive domains, yet prevailing human-in/on/out-of-the-loop categorizations fail to capture the quality of human-robot interaction (HRI). Meaningful Human Control (MHC) has emerged as a guiding principle, but its measurement remains under-specified. This paper presents a systematic review and measurement guide for operationalizing MHC in HRI, mapping its core constructs-trust, involvement, and situation awareness (SA)-to standardized self-report instruments. We review standardized questionnaires and related methods and compare their validity, reliability, and suitability for HRI user interface (UI) evaluation. We found that trust is well supported by validated scales, notably the MDMT and Schaefer’s Trust Perception Scale-HRI, with Jian’s Trust in Automated Systems scale as a widely used alternative. Involvement is best assessed via the UES felt-involvement subscale, with PQ/ITQ as viable complements. For SA, SAGAT and SARS are well-established tools, though many SA tools lack validation for HRI contexts. We offer a guide to measure MHC in HRI via standardized instruments, enabling UI comparison and adherence assessment. This operationalization supports the establishment of MHC in HRI design for sensitive domains.