Executive summary
The framework asks whether humans, AI systems and structured human-AI teams can be compared through a broader idea of reliability than factual accuracy alone. It considers calibration, source integrity, bias resistance, falsification, correction and auditability.
Three key takeaways
- Reliability includes uncertainty, source integrity and correction, as well as factual accuracy.
- Human and AI agreement can reflect shared assumptions rather than independent confirmation.
- Structured collaboration needs verification and human accountability; the proposed framework still requires empirical testing.
Proposed research questions
- How should a system express how strongly an answer deserves to be believed?
- When does agreement reflect independent confirmation rather than shared error?
- Which cognitive tasks should be assigned to machines and which remain human responsibilities?
- Can reliability be compared without hiding theoretical assumptions inside one score?
Limitations
The manuscript contains no original experimental dataset. The named concepts, dimensions and proposed weights remain conceptual and require independent empirical testing.
Competing interests and possible use
TAPiO may develop research, advisory methods, benchmarks or commercial services from these concepts. This is a potential commercial interest relevant to interpreting the proposed framework.