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Ontology-Based Contextual AI Evaluations (OB-CAIE) Methodology — arXiv:2610.00529

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.00529
License (as archived): CC0-1.0
Archived: 2026-10-06T12:02:08.538Z
SHA-256 of the archived text: d1580ca7c88cae5f915f1226192e5c137d0ae76086bc7473a197abd09eda88ff
Stable URL of this page: https://metraipolis.com/documento/dc_ffd35fcb4847

How to cite

Ontology-Based Contextual AI Evaluations (OB-CAIE) Methodology — arXiv:2610.00529 — arXiv (en), https://arxiv.org/abs/2610.00529 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ffd35fcb4847 (Metraipolis archive dc_ffd35fcb4847, sha256 d1580ca7c88cae5f915f1226192e5c137d0ae76086bc7473a197abd09eda88ff)

BibTeX

@misc{dcffd35fcb4847,
  title = {Ontology-Based Contextual AI Evaluations (OB-CAIE) Methodology — arXiv:2610.00529},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.00529},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ffd35fcb4847; sha256 d1580ca7c88cae5f915f1226192e5c137d0ae76086bc7473a197abd09eda88ff},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.00529. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The ontology-based contextual AI evaluation (OB-CAIE) methodology was developed to address a lack of scientific rigor that arises from unclear testing coverage, to balance human expertise and automations, and to address a lack of reproducibility of AI evaluation testing environments. OB-CAIE strengthens the current state of AI evaluations by addressing the first step in the scientific method by clearly defining what will be tested. Two ontologies represent the tractable problem space in the OB-CAIE methodology: the Domain-Specific Ontology (DSO) and the Evaluation Process Ontology (EPO). The DSO is the what; the EPO is the how. An OB-CAIE problem space can be used for one or multiple AI evaluations. The OB-CAIE methodology allows for human judgment at specific points, in scientifically grounded ways, and in complex subject areas where human feedback is genuinely irreducible or machine irreplaceable. A key advantage of the OB-CAIE methodology is that failure points can be traced, visualized and analyzed within the canonical OB-CAIE methodology problem space.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-02 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2610.00529 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T10:21:27.867Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).