[D] How should AI systems handle missing or conflicting evidence before making decisions?

[u/zerovariance36]

When an AI system encounters incomplete, inconsistent, or contradictory information, how should it determine whether there is enough evidence to produce a reliable answer?

This is an important problem in natural language processing, information extraction, and AI-assisted decision-making. A system may retrieve information from multiple sources, encounter missing values, or find conflicting claims that cannot be resolved using the available evidence.

One possible approach is to use evidence-first, fail-closed decision rules. Under this approach, a system would not proceed with scoring or evaluation when essential evidence or required conditions remain unresolved. Missing information would remain explicitly missing rather than being silently replaced with assumed values.

I am exploring these principles through the Organic Intelligence Protocol (OIP), a research-stage framework focused on evidence requirements, auditability, and claim discipline. The framework has not been independently validated as a general machine learning method, and its documented audits do not establish predictive superiority or verified real-world AI safety.

I would appreciate technical perspectives on three questions.

1. How can evidence sufficiency be formally defined and measured in an NLP pipeline?

2. What evaluation methods could determine whether fail-closed rules reduce unsupported conclusions without unnecessarily rejecting valid answers?

3. How should a system distinguish missing information, contradictory evidence, and genuine uncertainty?

I am particularly interested in approaches involving information extraction, knowledge representation, retrieval-augmented generation, and uncertainty evaluation.

What existing methods or experimental designs would be most appropriate for investigating these questions?

[u/T1lted4lif3]

Do humans function well with incomplete or contradictory evidence? Just a question on my part, can’t contribute to an answer to your question

[u/zerovariance36 → u/T1lted4lif3]

Absolutely.. Humans can also struggle with incomplete or conflicting evidence. The key research question is how to make artificial intelligence systems handle uncertainty transparently and avoid unsupported conclusions.

[u/Legal-Reporter1912]

I think keeping missing info separate instead of filling in the gaps with guesses makes a lot of sense. especially when sources contradict each other. the tricky part is knowing when theres actually enough evidence to move forward

[u/zerovariance36 → u/Legal-Reporter1912]

Agreed....The challenge is defining evidence sufficiency without turning caution into unnecessary rejection. A useful evaluation would measure both unsupported conclusions and valid answers incorrectly blocked, while keeping missing and conflicting evidence distinct. This is one of the questions I am investigating through OIP.

[u/Legal-Reporter1912 → u/zerovariance36]

yeah thats an interesting thing to look into, finding the right balance between being careful and still giving useful answer is important