Purpose

Artificial intelligence (AI) fosters economic growth and opens up new directions for innovation. However, the diffusion of AI proceeds very slowly and falls behind, especially in comparison to other technologies. An important path leading to better adoption rates identified is trust-building. Particular requirements for trust and their relevance for AI adoption are currently insufficiently addressed.

Design/methodology/approach

To close this gap, the authors follow a qualitative approach, drawing on the extended valence framework by assessing semi-structured interviews with experts from various companies.

Findings

The authors contribute to research by finding several subcategories for the three main trust dimensions ability, integrity and benevolence, thereby revealing fundamental differences for building trust in AI compared to more traditional technologies. In particular, the authors find access to knowledge, transparency, explainability, certification, as well as self-imposed standards and guidelines to be important factors that increase overall trust in AI.

Originality/value

The results show how the valence framework needs to be elaborated to become applicable to the AI context and provide further structural orientation to better understand AI adoption intentions. This may help decision-makers to identify further requirements or strategies to increase overall trust in their AI products, creating competitive and operational advantage.

中文翻译：

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我们可以相信人工智能吗？信任要求的实证调查和成功采用人工智能的指南

目的

人工智能 (AI) 促进经济增长并为创新开辟新方向。然而，人工智能的传播进展非常缓慢并且落后，尤其是与其他技术相比。一个导致更高采用率的重要途径是建立信任。目前尚未充分解决对信任的特殊要求及其与人工智能采用的相关性。

设计/方法/方法

为了缩小这一差距，作者采用定性方法，通过评估对来自不同公司的专家的半结构化访谈，利用扩展的价框架。

发现

作者通过为三个主要信任维度能力、诚信和仁慈找到几个子类别来为研究做出贡献，从而揭示与更传统的技术相比，在人工智能中建立信任的根本差异。特别是，作者发现获取知识、透明度、可解释性、认证以及自我强加的标准和指南是增加对人工智能的整体信任的重要因素。

原创性/价值

结果显示了需要如何详细说明价框架以适用于 AI 环境并提供进一步的结构方向以更好地理解 AI 采用意图。这可能有助于决策者确定进一步的要求或策略，以增加对其 AI 产品的整体信任，从而创造竞争和运营优势。