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Article In: Pragmatics and Society: Online-First Articles

Six guidelines for trustworthy, ethical and responsible automation design

ORCiD logo with link | University of Southern Denmark
ORCiD logo with link | Philipps-Universität Marburg
ORCiD logo with link | U.S. Air Force Academy
This content is being prepared for publication; it may be subject to changes.
Abstract
Calibrated trust (Lee, John D., and Katrina A. See. 2004. “Trust in Automation: Designing for Appropriate Reliance.” Human Factors 46 (1): 50–80. Google Scholar logo with link to Google Scholar) is critical for safe and seamless integration of automated systems 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, Nadine, Kevin Baum, Alarith Uhde, Sarah Sterz, Martin C. Hirsch, and Markus Langer. 2025. “How do we assess the trustworthiness of AI? Introducing the trustworthiness assessment model (TrAM).” Computers in Human Behavior 1701: 108671. Google Scholar logo with link to Google Scholar).
The field of pragmatics offers valuable insights into communication processes emphasizing the importance of context and mutual understanding in communication. These are essential for developing accurate trustworthiness assessments bridging the gap between user expectations and system capabilities.
We propose six design guidelines to help designers foster accurate trustworthiness assessments, and thus ethical and responsible human-automation interactions. The guidelines draw on human-computer interaction, cognitive psychology, automation research, user-experience design, ethics, as well as pragmatics — specifically, the cultivation of common ground (Clark, Herbert. 1996. Using Language. Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar) and Gricean communication maxims (Grice, Herbert P. 1975. “Logic and Conversation.” In Speech Acts, ed. by Peter Cole and Jerry L. Morgan, 41–58. New York: Academic Press. Google Scholar logo with link to Google Scholar). Pragmatic principles provide valuable insights into these contexts, because the user’s perception of the system’s trustworthiness is shaped by both environmental contexts, such as organizational culture or societal norms, but also situational context, including the specific circumstances or scenarios in which the interaction occurs (Hoff, Kevin A., and Masooda Bashir. 2015. “Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust.” Human Factors 57 (3): 407–34. Google Scholar logo with link to Google Scholar).
The guidelines provide actionable insights for designers to create automated systems that make relevant trustworthiness cues available and also serve as a tool for evaluating to what extent existing systems enable users to accurately assess a system’s trustworthiness.
Article outline
  • 1.Introduction
    • 1.1Significance of trust in human-automation interactions
    • 1.2Pragmatics as a framework for trust calibration
    • 1.3Effective communication as the foundation of an accurate trustworthiness assessment
  • 2.Guidelines
    • Guideline 1 — Allow verification of automation operation
      • Design guidance
      • Supporting evidence
      • Example
      • Caution
    • Guideline 2 — Explain automation actions
      • Design Guidance
      • Supporting evidence
      • Example
      • Caution
    • Guideline 3 — Convey automation uncertainty
      • Design guidance
      • Supporting evidence
      • Example
      • Caution
    • Guideline 4 — Provide methods to recover from automation errors
      • Design guidance
      • Supporting evidence
      • Example
      • Caution
    • Guideline 5 — Design automation to conform with social norms and etiquette
      • Design Guidance
      • Supporting evidence
      • Example
      • Caution
    • Guideline 6 — Provide user training
      • Design guidance
      • Supporting evidence
      • Example
      • Caution
  • 3.Discussion
    • 3.1Contributions
    • 3.2The need for trustworthiness heuristics
    • 3.3How previous lists are not adequate
    • 3.4Implications and future research directions
  • 4.Conclusion
  • Notes
  • References
References (95)
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