Cited by Trust-Building in AI-Human Partnerships Within Industry 5.0

Trust-Building in AI-Human Partnerships Within Industry 5.0 DOI

System Safety Human - Technical Facility - Environment, Journal Year: 2024, Volume and Issue: 6(1)

Published: Dec. 1, 2024

Abstract The rapid advancement of artificial intelligence (AI) within Industry 4.0 has transformed manufacturing processes, shifting from traditional automation to more collaborative AI-human partnerships. While AI promises enhanced efficiency, precision, and productivity, the success these systems relies heavily on trust established between human operators technologies. This paper explores critical factors influencing in partnerships sector, emphasizing need for transparency, accountability, ethical design. Drawing a multi-disciplinary literature review empirical studies, we identify key drivers trust, including preferences system explainability decisions, reliability dynamic production environments. Furthermore, examines challenges associated with trust-building, such as overcoming fear job displacement managing perceived risks errors. findings contribute growing body knowledge human-centric design offer practical recommendations fostering ensure successful collaboration settings. By transitioning purely automated partnerships, manufacturers can unlock full potential while maintaining workforce that is confident AI’s alignment.

Language: Английский

Human-LLM collaboration in generative design for customization DOI

Xingzhi Wang, Zhoumingju Jiang, Yi Xiong

et al.

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 80, P. 425 - 435

Published: March 30, 2025

Language: Английский

Citations

A novel idea generation tool using a structured conversational AI (CAI) system DOI

B. Sankar,

Dibakar Sen

Artificial intelligence for engineering design analysis and manufacturing, Journal Year: 2025, Volume and Issue: 39

Published: Jan. 1, 2025

Abstract This article presents a novel conversational artificial intelligence (CAI)-enabled active ideation system as creative idea generation tool to assist novice product designers in mitigating the initial latency and bottlenecks that are commonly observed. It is dynamic, interactive, contextually responsive approach, actively involving large language model (LLM) from domain of natural processing (NLP) (AI) produce multiple statements potential ideas for different design problems. Integrating such AI models with creates what we refer an scenario , which helps foster continuous dialog-based interaction, context-sensitive conversation, prolific generation. An empirical study was conducted 30 generate given problems using traditional methods new CAI-based interface. The generated by both were qualitatively evaluated panel experts. findings demonstrated relative superiority proposed generating prolific, meaningful, novel, diverse ideas. interface enhanced incorporating prompt-engineered structured dialog style each stage make it uniform more convenient designers. A pilot resulting responses CAI found be succinct aligned toward subsequent stage. thus established rich generative (Gen-AI) early ill-structured phase process.

Language: Английский

Citations

AutoTRIZ: Automating engineering innovation with TRIZ and large language models DOI