Terms and disclaimers

1. What Jiddu does

Jiddu is an automated tool. It uses large language models (currently routed through OpenRouter to OpenAI, Anthropic, MiniMax and Perplexity Sonar) to (a) flag passages that may contain logical fallacies and (b) extract verifiable factual claims from text and offer a verdict based on a web search.

Jiddu does not employ human editors. Every output you see is produced by software. The tool is meant to assist critical reading — not to replace it.

2. No claim of truth

The verdicts ("supported", "contradicted", "mixed", "unverified") and the fallacy labels are best-effort automated judgements. They can be wrong — sometimes confidently wrong.

Jiddu does not assert that any statement, person, or document is true, false, lying, or deceitful. The labels are signals to prompt your own verification. Always read the cited sources and form your own conclusion.

3. Use at your own risk

You are responsible for how you use the analysis and what you share or publish. If you republish a Jiddu result, you adopt that content as your own — Jiddu's authors are not responsible for downstream uses.

Do not submit confidential, proprietary, or personal data. Submitted text is forwarded to the model providers above for processing and stored under a shareable id.

4. Persons and public figures

Analyses about named persons (politicians, journalists, executives) are produced by software searching public web sources. The language Jiddu uses is intentionally cautious ("evidence does not support", "we could not find") — never adopt a stronger framing than the tool emits.

If you believe a verdict is unfair or unsupported, use the "report wrong verdict" button on the relevant claim. Repeated unfair flags against the same person may be removed at the operator's discretion.

5. Privacy

Each analysis is assigned a short id and stored on a server in Brazil. Anyone with the URL can view it. There are no user accounts.

We log the request IP for rate-limiting and abuse detection. We do not sell or share that information.

Page traffic is measured via Google Analytics (gtag.js).

6. Methodology

Our claim extraction and verification pipeline is inspired by Claimify (Metropolitansky & Larson, MSR 2025) for the extraction stage, and by the evaluation framework described in Distilling Expert Judgment at Scale (Goldfarb, Hall, Fisher, Salam, Wilde — Forum AI / Stanford, 2025) for the verdict assessment, source-quality tiers and neutrality dimension. Our choice of 4 verdict classes (supported / contradicted / mixed / unverified) rather than the 5- or 6-class schemes used by some fact-checking outlets is informed by Sahitaj et al. 2025, who found that 3-class labeling outperforms 5-class for LLM-based fact-checking — the additional categories in the middle introduce ambiguity without improving accuracy.

Jiddu is not affiliated with any of those research groups; we cite them because the methodology behind the tool is grounded in their work, and users should know that the design choices have an academic basis.

The fact-check pipeline has been benchmarked against the PolitiFact human-labeled corpus (via the LIAR2 dataset, Apache-2.0). On 200 claims from PolitiFact's polar buckets, Jiddu's verdict matched the human verdict in 67.5% of cases overall and 81.3% on the unambiguous true / false / pants-on-fire buckets, with strict polar disagreement in only 4.5%. Full methodology, confusion matrix and disagreement analysis at docs/benchmark-politifact.md.

7. Contact

Operator: Rafael de Menezes Ehlers. For takedown requests, corrections, or other concerns, contact via LinkedIn (linked in the footer).