mlsanityData to 14 Oct 2026

Six Guidelines for Trustworthy, Ethical and Responsible Automation Design

arXiv 2508.02371 ↗First posted 4 Aug 2025cs.HC cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Calibrated trust in automated systems (Lee and See 2004) is critical for their safe and seamless integration into society. Users should only rely on a system recommendation when it is actually correct and reject it when it is factually wrong. One requirement to achieve this goal is an accurate trustworthiness assessment, ensuring that the user's perception of the system's trustworthiness aligns with its actual trustworthiness, allowing users to make informed decisions about the extent to which they can rely on the system (Schlicker et al. 2022). We propose six design guidelines to help designers optimize for accurate trustworthiness assessments, thus fostering ethical and responsible human-automation interactions. The proposed guidelines are derived from existing literature in various fields, such as human-computer interaction, cognitive psychology, automation research, user-experience design, and ethics. We are incorporating key principles from the field of pragmatics, specifically the cultivation of common ground (H. H. Clark 1996) and Gricean communication maxims (Grice 1975). These principles are essential for the design of automated systems because the user's perception of the system's trustworthiness is shaped by both environmental contexts, such as organizational culture or societal norms, and by situational context, including the specific circumstances or scenarios in which the interaction occurs (Hoff and Bashir 2015). Our proposed guidelines provide actionable insights for designers to create automated systems that make relevant trustworthiness cues available. This would ideally foster calibrated trust and more satisfactory, productive, and safe interactions between humans and automated systems. Furthermore, the proposed heuristics might work as a tool for evaluating to what extent existing systems enable users to accurately assess a system's trustworthiness.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 71 references that are in the map. First posted on arXiv 4 Aug 2025.

Builds on

6 of 71 references.

References · 6

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  2. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  3. 2022Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learningcited by 53
  4. 2015The social dilemma of autonomous vehiclescited by 48
  5. 2022An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Makingcited by 7
  6. 2024"Oh, Sorry, I Think I Interrupted You'': Designing Repair Strategies for Robotic Longitudinal Well-being Coachingcited by 3

Built on by

Not cited yet.