Dan Russell writes about distinguishing between true, correct, and valid claims and offers a six-step verification routine for navigating misinformation. He notes that people often confuse fluency with factuality and that each of the three questions—truth, correctness, and validity—fails in distinct ways requiring different checks. His practical steps include pinning down the claim, noticing emotional reactions, tracing evidence upstream, reading laterally, triangulating with independent sources, and calibrating confidence. He also addresses the specific challenge of verifying what AI tells you, noting that fluent, confident outputs break usual credibility cues, and recommends treating citations as claims to be checked rather than evidence to be trusted.
- The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
- Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
- AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
- The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.
- The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
- Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
- AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
- The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.
This article discusses how to conduct long-term research effectively using AI as a partner, moving beyond single-prompt queries. It emphasizes the need for "Long-Term Triangulation" – a continuous, iterative methodology. The author outlines four key pillars: building a persistent memory for the AI, tracking shifts in the AI's understanding, actively critiquing its responses with contradictory data, and performing meta-audits to identify blind spots in the research process. The goal is to foster productive friction and avoid intellectual echo chambers, ensuring both the human and the AI think critically.
FailSafe is an open-source, modular framework designed to automate the verification of textual claims. It employs a multi-stage pipeline that integrates Large Language Models (LLMs) with retrieval-augmented generation (RAG) techniques.
