Reinvigorating Structured Knowledge and Ontologies for Trustworthy and Beneficial AI and Robotics
This position paper contends that the rapid advancement of artificial intelligence and robotics has outpaced the development of structured knowledge and formal ontologies, creating a semantic gap that hinders verification and transparency. The authors argue that formal ontology research must be reinvigorated through renewed academic investment, reintegration into development workflows, and lower barriers to adoption. Specifically, they advocate for ontology-augmented verification methods, semantic auditing practices, and FAIR publishing standards to make autonomous systems more accountable and beneficial.
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 improving the transparency, verification, and trustworthiness of AI and robotics systems through formal ontologies.
What the document contains
This paper addresses the lag in structured knowledge and formal ontology development relative to AI and robotics advancements, proposing methods such as semantic auditing, ontology-augmented verification, and FAIR publishing standards.
The document does not provide binding regulations, legal enforcement mechanisms, or formal technical standards specifications.
- The rapid growth of AI and robotics has created a significant semantic gap due to under-resourced formal ontology research.
- Opaque and difficult-to-verify autonomous systems pose substantial risks that require structured knowledge to address.
- Formal ontologies should be treated as core contributors to trustworthy AI and robotics design rather than mere support functions.
- The authors advocate for ontology-augmented verification methods to incorporate semantic constraints into behavioral validation.
- Institutionalizing semantic auditing and FAIR publishing standards for ontologies is essential for transparency and accountability.
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
ensure that autonomous AI/robotics systems remain not only performant but also transparent, accountable, and ultimately beneficial to humanity.
Abstract
screening score · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
Abstract
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
Abstract
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
Abstract
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
Abstract
screening score · 2026-10-23
Transparency
2/5Openness about how AI systems work and are used
for the institutionalization of semantic auditing practices that allow ontologies to serve as transparent, inspectable system referents
Abstract
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
Abstract
screening score · 2026-10-23
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
Abstract
screening score · 2026-10-23