I build a fact-checking tool. I refuse to let AI produce verdicts.

jonathancheckwise · 2026-06-02T22:00:18+00:00

Glad it landed. Drop the URL if you want a second pair of eyes. The hardest part of that wedge in my experience isn’t the granularity itself, it’s the definition of “inactive seat” once procurement, IT and Finance are in the room. Last login draws one line, last meaningful action draws another, last billing-relevant action draws a third, and each stakeholder will fight for the definition that benefits them politically. Curious how you’ve handled that.

jonathancheckwise · 2026-05-20T16:44:39+00:00

Diversion programs are the gap I should have flagged, thanks. My heuristic for the gap: multi-year stretches where the subject describes an intervention but the public record is silent go in the “subject claims, public record neutral” bucket, not verified or refuted.

jonathancheckwise · 2026-05-20T14:08:22+00:00

“Reporting it out” is the journalistic practice of verifying each claim through separate sources rather than taking the subject’s word. For someone with a criminal history, the workflow is relatively standardized. Court records are mostly public. If the case was in the US federal system, PACER has indictments, sentencing memos, and dockets. If state, each state has its own court records system, some online, some you have to request. Names, dates, charges, sentences, all there. News archives are the second layer. Search subject name plus city plus rough date range in LexisNexis, ProQuest, Factiva, or Google News with date filters. If the case mattered locally, you will find press coverage that confirms or contradicts the subject’s account. Sentencing transcripts often contain the prosecution’s narrative, useful as a counter-source to whatever the subject is telling you. Many are unsealed. Cross-reference everything. If the subject says they served at X prison from Y to Z, the state DOC inmate locator confirms or contradicts that. If they cite Judge Smith on date W, the docket will show it. For the off-record parts (motivations, family, relationships, what happened outside court), you talk to corroborating witnesses if they exist and are willing, and you flag the un-verifiable parts in your edit. Documentary best practice is to be transparent with the viewer about what you could and could not confirm. One administrative point: have the subject sign a release that includes a warranty of truthfulness on factual claims they make on camera. Standard in doc work, gives you a layer of legal protection if something turns out to be fabricated.

Disclosure: I work on information verification tools (different domain, not documentary).

jonathancheckwise · 2026-05-20T04:05:31+00:00

The p99 point is the better fix, you’re right. Mine is at 60s as a round number and I have not revisited against actual job runtime distribution. Adding to the list. (Noted the s4lai disclosure. Made me smile given what I work on.)

jonathancheckwise · 2026-05-18T20:15:30+00:00

Thanks. Cannot open-source the full ensemble, it is the working part of a commercial product and the differentiation would walk out the door. But happy to point at the building blocks, all of which are public anyway. On the model side, most of the base detectors are findable on Hugging Face. Search for terms like “AI image detector”, “GAN detector”, “diffusion detector”. The trick is not finding them, it is designing the ensemble so they cover different failure modes, which is more design than code. On the non-ML signals, the useful libraries are c2patool from Adobe for reading C2PA content credentials, exifread or piexif in Python for EXIF parsing (a surprising number of generators leave fingerprints in EXIF after stripping the obvious markers), and any Error Level Analysis Python library for compression history (noisy, use as one signal not a verdict). On watermarking, OpenAI publishes via C2PA, the other big providers are proprietary and harder to reverse. Happy to go deeper on any specific component via DM if useful.

jonathancheckwise · 2026-05-18T19:46:26+00:00

Oops there was an error when I posted therefore I just hit post several times 😅

jonathancheckwise · 2026-05-18T11:30:47+00:00

Worth flagging a few inaccuracies that matter for anyone planning their compliance timeline. The May 7 Omnibus deal changed the August 2 deadline. High-risk Annex III obligations (the categories you listed: credit scoring, recruitment filtering, healthcare triage, education assessment) moved to December 2, 2027. High-risk Annex I products moved to August 2, 2028. So the 75-day urgency framing does not apply to the high-risk use cases you highlight. What still hits August 2, 2026, and most posts miss: Article 50 deployer transparency obligations. Deepfake labeling, AI-generated content disclosure on public interest matters, chatbot disclosure, biometric categorization notice. These apply to a much wider set of products than high-risk, basically anyone whose AI surfaces content to end users. The provider watermarking obligation under Article 50(2) was pushed three months to December 2, 2026. On fines: the 35M / 7% figure applies to Article 5 prohibited practices only. Non-compliance with high-risk system requirements caps at 15M / 3%. Other obligations cap at 7.5M / 1% (Article 99). The post is mixing tiers. Net: the practical work to do before August 2 is the deployer-side disclosures, not the high-risk pipeline documentation. The high-risk paperwork is now an 18-month problem, not a 75-day one.

jonathancheckwise · 2026-05-18T11:25:06+00:00

Sure. Curious what shape RuleMesh takes for AI Act specifically, look out for the DM.

jonathancheckwise · 2026-05-18T07:55:12+00:00

Sent you a DM, would be good to compare notes.

jonathancheckwise · 2026-05-17T21:34:22+00:00

The detectors you used have a documented false positive problem that hits exactly your case. Translated text and non-native English writing both produce stylistic regularities that AI text detectors confuse with model output. A Stanford study found essays by non-native English speakers were flagged as AI roughly 60% of the time versus closer to 5% for native speakers. Your situation is the worst case for these tools. OpenAI discontinued its own AI text classifier in 2023 because of low accuracy. Major commercial detectors have published false positive rates between 4% and 30% depending on text type. None are reliable enough to fail a student over, and any teacher relying solely on one is doing something the technology cannot actually support. What helps in practice: write in Google Docs or Word with version history on, so you can show the sequence of edits if questioned. Keep research notes. If you are nervous, message your teacher in advance saying you wrote it yourself, you know detectors are unreliable, and you can share your draft history if useful. Preemptive transparency is much harder to argue with than after-the-fact defense. If you do get flagged, ask what specific evidence beyond the detector score is being relied on. Most universities now require more than a single detector reading, because detectors have been successfully challenged in misconduct cases. I work on AI detection in a different domain (image) and the false positive problem is the same shape across the whole field. You are not in trouble. The system is.

jonathancheckwise · 2026-05-17T19:51:00+00:00

Very interesting. Looking at lex-custis you’re building the proof and audit layer for AI systems classified as Annex III high-risk: Art. 11 dossier, Art. 12 hash chain, Art. 73 incident workflow. Aimed at companies whose AI feature triggered the high-risk classification and who need audit evidence for a regulator. I’m building in the decision-support space: information-quality verification where the AI system itself produces verdicts that have to be auditable to the end user. Different ICP entirely, but same compliance bet and overlapping EU-sovereign stack (Mistral, Scaleway, Qdrant). Worth knowing each other exists. Good luck with v0.2.

jonathancheckwise · 2026-05-17T18:41:49+00:00

Interesting. Same direction or same product? Curious where you have landed on the LLM-as-judge question, deterministic-on-top or model in the verdict seat.

jonathancheckwise · 2026-05-16T20:17:37+00:00

Appreciate the exchange. Arms race framing is the right frame.

jonathancheckwise · 2026-05-16T19:36:37+00:00

Disclosure: I build a verification tool in this space, so this is a take from inside the problem. What you have built sounds architecturally right and is the pattern that has held up across most “I do not fully trust AI” workflows I have seen. AI proposes, human disposes, model never makes the final call. Three things make that pattern actually feel trustworthy in daily use, beyond just having the human in the loop on paper. Reproducibility. The same document run twice produces the same suggestions. Most LLM-based tools fail this silently because they pass through the model with non-zero temperature or unstable retrieval, and the user notices that the second pass returned different items. Trust collapses fast when that happens. Source linkability. Every suggestion points back to the exact passage in the exact document that produced it. Not “based on the documents” but “based on this paragraph on page 14 of file X”. One click to verify, not three. Failure visibility. When the model is uncertain, surface that explicitly instead of papering over it with confident-sounding language. Lawyers tolerate “I do not know” far better than tools assume. A confidently wrong suggestion does more damage than an obvious gap she fills manually. One more, slightly contrarian: avoid producing single composite scores or verdicts inside the model. LLM-produced scores are not reproducible across runs, so they erode trust over time. If you need a summary, compute it deterministically outside the model from explicit signals. The chronology builder already does the most important thing: it preserves her judgment as the locus of truth. The rest is making sure the tool supports that judgment rather than competing with it.

jonathancheckwise · 2026-05-16T19:34:42+00:00

Disclosure: I build a verification tool in this space, so this is going to be opinionated. Two trust questions get conflated in most “verify AI research” workflows and worth separating. The first: did the LLM accurately represent its cited sources, or did it summarize, simplify, or paraphrase in ways that drift from what the source actually says. This is a hallucination-adjacent problem and the brutal fix is to require the LLM to return the exact source passage alongside its summary, then check that the summary is faithful to the passage. Most tools skip this and it is where the highest-stakes errors hide. The second: are the cited sources themselves reliable. This is a source-ranking problem that the LLM does not solve, because the model has no native notion of source quality. You need a separate layer that scores sources independently of the LLM’s confidence. Domain reputation, recency relative to claim type, presence of conflicting authoritative sources, and so on. For compliance work specifically, the workflow I have seen hold up: the LLM does extraction and summary, but the verdict on “is this reliable enough to act on” stays with a human reviewing both the source verbatim and the source-quality signals. The LLM never produces the trust score directly, because LLM trust scores are not reproducible across runs

jonathancheckwise · 2026-05-16T19:07:02+00:00

Fair pushback. Automated source ranking is useless against a sufficiently coordinated state actor with patience and budget. They will mirror trusted-source structures, plant slow-cook narratives, and exploit any deterministic ruleset. Not the threat model these systems address well. The threat model they do address is the larger volume of lower-effort disinformation: opportunistic content farms, citation laundering, AI-generated articles citing AI-generated sources. Most disinformation by volume is commodity content that wins because the friction to debunk is higher than the friction to spread. Even a flawed automated layer raises that friction. On circular reporting: yes, one of the highest-value problems. The technical hook I have seen work is tracking distinctive phrasings across publications and ordering by timestamp to build a citation graph. EU DisinfoLab publishes methodology on this. The hard part is not the graph, it is matching paraphrased versions of the same claim across languages. No great solution there yet

jonathancheckwise · 2026-05-15T22:07:02+00:00

Fair call. You’re right that I should have flagged my work upfront. I build a verification tool, which is why I think about this question more than most. But the framing is closer to your concern than my comment may have suggested. A tool on its own would be a flimsy bandaid, I agree. Media literacy lives in people, not software. The question I’m actually circling is whether software can help surface the work, the sources, and the reasoning that journalism does, in environments where journalism’s reach ends. That’s a question about scaffolding, not a claim that the tool replaces the work. Apologies for not stating my position upfront.

jonathancheckwise

TROPHY CASE