Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI
This qualitative and philosophical study explores how individuals experience, define, and reason about trust in autonomous systems with evolving functionality (ASEFs). Through semi-structured interviews with developers, end users, and other stakeholders using specific use cases, the authors examine five philosophical accounts of trust: rational, affective, credentialist, norms-based, and relational. The findings reveal that while technical reliability and design aesthetics play roles in user reliance, the most significant requirement for trust and trustworthiness is the accountability of human developers and operators, supported by perceived value alignment and regulatory mechanisms.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, developers, and policymakers interested in the philosophical and empirical dimensions of trust and accountability in artificial intelligence and autonomous systems.
What the document contains
The study covers an empirical and philosophical investigation into how developers and end users conceptualize trust and trustworthiness in autonomous systems with evolving functionality. It addresses five specific accounts of trust and highlights the central role of human accountability, value alignment, and regulatory oversight.
The document does not provide binding legal regulations, mandatory industry standards, or formal definitions enforceable by law for AI development.
- Individuals reason about trust in autonomous systems using multiple philosophical accounts including rational, affective, credentialist, norms-based, and relational accounts.
- The physical design and aesthetics of autonomous systems can manipulate user affect and create deceptive feelings of familiarity or trust.
- Technical reliability and user reliance alone do not equate to interpersonal trust; true trustworthiness hinges on identifiable human accountability.
- The absence of clear moral agents and accountable actors for autonomous systems engenders suspicion and distrust among end users.
- Value alignment, institutional safeguards, and regulatory mechanisms are critical components that allow users to feel their interests are protected.
· Introduction · Methods · Findings · Discussion · Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
5/5Clear responsibility for AI outcomes
Ultimately, the most significant requirement for user trust and assessment of trustworthiness was the accountability of AI developers for the outputs of AI systems
Abstract
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-24
Human Oversight
2/5Humans remain in control of consequential decisions
you’re probably still going to be there with a big emergency stop button
Findings
screening score · 2026-10-24
Privacy
2/5Protection of personal data and private life
even though perhaps we have no idea how they're going to store our data, who they're going to share them with.
Findings
screening score · 2026-10-24
Transparency
2/5Openness about how AI systems work and are used
This type of trust is markedly different from the simple experience-based reliance characterized above. While also rational in nature, the basis of this form of trust is not past experience, but the relative explicability of the technology.
Findings
screening score · 2026-10-24
Enforcement
2/5Consequences and compliance mechanisms with real force
It was often acknowledged that systemic regulation was needed, utilizing incentives and sanctions to motivate responsible innovation.
Findings
screening score · 2026-10-24
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
screening score · 2026-10-24