Scholarlink

<p>As autonomous systems (e.g., AI-enabled vehicles, robotics, and decision-support platforms) increasingly shape factories, transport, and digital infrastructures, embedding Responsible AI principles has become essential. This study investigates organizational adoption of Responsible AI, focusing on three drivers: societal expectations (Institutional Pressures), strategic business priorities (Business Validity), and system-level trustworthiness (System Trustworthiness). Adoption is seen not only as a technical issue but also as a response to external legitimacy demands and internal business imperatives. A cross-sectional survey of 350 professionals in technology, analytics, and digital transformation (primarily in Asia and the Americas) was analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that business priorities are the strongest driver of adoption, with trustworthiness providing additional reinforcement. Institutional Pressures, though modest in their direct effect, influence adoption more substantially through their indirect effects via business priorities and trustworthiness. The study offers guidance for managers on aligning Responsible AI with business strategy, for policymakers on shaping legitimacy frameworks, and for system designers on embedding trust features such as explainability and fairness.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Signals, systems, and strategy: understanding responsible AI in autonomous environments

  • 期刊论文

  • 发表日期：

    2025年12月4日

查看全文
  • uday nedunuri,
  • Abhijitdas Gupta,
  • Debashis Guha

摘要

As autonomous systems (e.g., AI-enabled vehicles, robotics, and decision-support platforms) increasingly shape factories, transport, and digital infrastructures, embedding Responsible AI principles has become essential. This study investigates organizational adoption of Responsible AI, focusing on three drivers: societal expectations (Institutional Pressures), strategic business priorities (Business Validity), and system-level trustworthiness (System Trustworthiness). Adoption is seen not only as a technical issue but also as a response to external legitimacy demands and internal business imperatives. A cross-sectional survey of 350 professionals in technology, analytics, and digital transformation (primarily in Asia and the Americas) was analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that business priorities are the strongest driver of adoption, with trustworthiness providing additional reinforcement. Institutional Pressures, though modest in their direct effect, influence adoption more substantially through their indirect effects via business priorities and trustworthiness. The study offers guidance for managers on aligning Responsible AI with business strategy, for policymakers on shaping legitimacy frameworks, and for system designers on embedding trust features such as explainability and fairness.