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In this paper the current challenging problem of Artificial Intelligence transparency and trustworthiness are addressed. Explainable Artificial Intelligence has been conceptualized as a key to making AI systems transparent, accountable, and compliant with human rights standards. If not transparent and trustworthy, AI systems cannot be extensively used and widely adopted in certain domains and applications, such as human-robot collaboration, healthcare etc. Therefore, as a practical takeaway and major contribution of this paper, a new checklist for AI Transparency is proposed and showcased in the practical human-robot collaboration (HRC) use-case. It is recommended that the checklist be utilized by AI system designers and developers to ensure transparency to end users and the trustworthiness of AI systems assisting humans.

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Transparent and Trustworthy eXpainable AI (xAI) in Smart Human-Robot Collaboration Environment

  • 会议论文

  • 发表日期：

    2026年

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  • Michał Choraś,
  • Aleksandra Pawlicka,
  • Marek Pawlicki,
  • Rafał Kozik

摘要

In this paper the current challenging problem of Artificial Intelligence transparency and trustworthiness are addressed. Explainable Artificial Intelligence has been conceptualized as a key to making AI systems transparent, accountable, and compliant with human rights standards. If not transparent and trustworthy, AI systems cannot be extensively used and widely adopted in certain domains and applications, such as human-robot collaboration, healthcare etc. Therefore, as a practical takeaway and major contribution of this paper, a new checklist for AI Transparency is proposed and showcased in the practical human-robot collaboration (HRC) use-case. It is recommended that the checklist be utilized by AI system designers and developers to ensure transparency to end users and the trustworthiness of AI systems assisting humans.