evaluative-reasoning-deception

To assess claim credibility using evidence-based reasoning, input the claim with contextual metadata, supporting and contradicting evidence, and the system generates a probabilistic assessment with explicit reasoning and recommended next actions.

Detect deception in investigative interviews by applying cognitive-load tactics, unexpected questions, and SUE-style evidence confrontation, avoiding common myths and cognitive biases through a structured, scientific framework.

Bayesian updating for fact-checking works by processing a phase-based workflow: clarifying claims, identifying premises, assessing evidence quality, generating alternative hypotheses, and updating probabilistic confidence accordingly.

Yes, you can use structured evaluative reasoning for hiring decisions and compliance reviews by inputting candidate claims or compliance statements, evaluating supporting evidence, and receiving transparent probabilistic judgments.

Limitations of probabilistic judgment in deception detection include explicit priors, likelihood limits of inference, and ethical constraints designed to minimize false positives, requiring transparent reasoning rather than absolute conclusions.