超越人工智能驱动的上下文感知服务:人类与人工智能协作的作用

Na Jiang, Xiaohui Liu, Hefu Liu, E. Lim, Chee‐Wee Tan, Jibao Gu
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引用次数: 1

摘要

人工智能(AI)近年来获得了显著的发展势头。在人工智能系统中,一个突出的应用是上下文感知系统。尽管人工智能和上下文感知的融合催生了个性化和及时的人工智能上下文感知系统,但仍然存在一些挑战。鉴于人工智能的“黑箱”性质,作者提出,人类与人工智能的协作对于人工智能驱动的上下文感知服务消除不确定性和发展至关重要。为此,本研究旨在推进人工智能驱动的上下文感知服务中人类与人工智能协作的促进因素和成果的研究议程。设计/方法/方法综合现有的关于人工智能和上下文感知的文献,作者提出了一个理论框架,该框架不仅区分了人工智能驱动的上下文感知服务的三个阶段(即上下文获取、上下文解释和上下文应用),而且还概述了每个阶段的合理研究方向。作者深入研究了人类与人工智能协作的作用,并从两个方向得出了未来的研究问题,即人工智能驱动的上下文感知服务设计对人类与人工智能协作的影响以及人类与人工智能协作的影响。原创性/价值本研究通过识别人工智能驱动的上下文感知服务中人类与人工智能协作的知识缺口,并提出相应的研究方向,为现有文献做出贡献。反过来,他们提出的框架为基于人工智能的上下文感知服务设计人员和从业者提供了可操作的指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beyond AI-powered context-aware services: the role of human–AI collaboration
PurposeArtificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.Design/methodology/approachSynthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.FindingsThe authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.Originality/valueThis study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.
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