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与(人工)同事一起工作时,要挑战被认为的技术优势。

Challenging presumed technological superiority when working with (artificial) colleagues.

机构信息

Department of Psychology and Ergonomics, Technische Universität Berlin, Marchstr. 12, F7, 10587, Berlin, Germany.

出版信息

Sci Rep. 2022 Mar 8;12(1):3768. doi: 10.1038/s41598-022-07808-x.

Abstract

Technological advancements are ubiquitously supporting or even replacing humans in all areas of life, bringing the potential for human-technology symbiosis but also novel challenges. To address these challenges, we conducted three experiments in different task contexts ranging from loan assignment over X-Ray evaluation to process industry. Specifically, we investigated the impact of support agent (artificial intelligence, decision support system, or human) and failure experience (one vs. none) on trust-related aspects of human-agent interaction. This included not only the subjective evaluation of the respective agent in terms of trust, reliability, and responsibility, when working together, but also a change in perspective to the willingness to be assessed oneself by the agent. In contrast to a presumed technological superiority, we show a general advantage with regard to trust and responsibility of human support over both technical support systems (i.e., artificial intelligence and decision support system), regardless of task context from the collaborative perspective. This effect reversed to a preference for technical systems when switching the perspective to being assessed. These findings illustrate an imperfect automation schema from the perspective of the advice-taker and demonstrate the importance of perspective when working with or being assessed by machine intelligence.

摘要

技术进步正在生活的各个领域广泛地支持甚至取代人类,带来了人类与技术共生的潜力,但也带来了新的挑战。为了应对这些挑战,我们在不同的任务环境中进行了三项实验,从贷款分配到 X 光评估再到流程工业。具体来说,我们研究了支持代理(人工智能、决策支持系统或人类)和失败经验(有或没有)对人机交互中与信任相关的方面的影响。这不仅包括在合作时对各自代理在信任、可靠性和责任方面的主观评估,还包括对代理评估自身意愿的视角转变。与技术优势的假定相反,无论从协作的角度来看,我们都展示了人类支持相对于两种技术支持系统(即人工智能和决策支持系统)在信任和责任方面的一般优势,而不是任务上下文。当视角切换到被评估时,这种效果又转而倾向于技术系统。这些发现从接受建议者的角度说明了自动化模式的不完美,并说明了在与机器智能合作或被其评估时视角的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/828d/8904495/e844c4851e47/41598_2022_7808_Fig1_HTML.jpg

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