AdalatAI：AI 輔助法院工作流程，AI 不定罪、不判刑
AdalatAI — AI-Assisted Court Workflow Where the AI Never Convicts and Never Sentences
AdalatAI 提供 AI 輔助法院工作流程，AI 只提出建議並附上引文，由法官決定及簽署，不負責定罪或判刑。系統引用官方法例原文，分類器在公開的合成語料留出集上 micro-F1 為 0.997，BM25 的 recall@5 為 1.0。工作流程以可逐項審核的規則為主，並保留完整審計紀錄。
The problem
4,742,731 pending cases (Supreme Court data, Dec 2024) — roughly half a century to clear at current rates. Records have been digitized since eCourt launched in 2009, but understanding hasn't been: litigants can't easily read their own case status, and no AI assists the workflow safely.
Legal AI has a trust cliff: a wrong-but-confident answer is worse than no answer. So AdalatAI is built around a different principle:
AI never convicts and never sentences. It suggests and cites; the human judge decides and signs — and every suggestion is auditable forever.
Try it: rudra496.github.io/adalatai
What it does
- 🗂 Case intake — narrate facts in Bangla or English (voice supported) → AI suggests case types and applicable Penal Code sections with verbatim statute quotes and punishment ranges parsed from the official text
- ⚖ Judge console — review every AI suggestion → edit / approve / override → sign; complete audit trail
- 📜 Judgment draft assembler — drafts are assembled only from verified parts (facts + verbatim statute + statutory range); the judge edits and signs
- 📚 Law browser — search all 547 sections (official bdlaws numbering), verbatim, with source links
- 📊 Pendency analytics — verified backlog numbers with sources
The zero-invention guarantees
This is the part I care about most, so it's engineered, not promised:
- Statute text is verbatim from bdlaws.minlaw.gov.bd (the official Ministry of Law site), fetched and integrity-tested — there are automated statute-verbatim gates in the test suite that fail if any AI output paraphrases the law
- Classifier: held-out micro-F1 0.997 on a disclosed synthetic corpus, with per-class thresholds calibrated on hand-written natural FIR sentences
- Retrieval: BM25, recall@5 = 1.0 through the real pipeline
- Evidence: 100 Crossref-verified papers (
docs/EVIDENCE.md) plus a 100-entry dataset registry with live URL checks - 18 automated tests, including the verbatim gates; the improve-loop (train → evaluate → critic → remediate) is traced in
case_model.json
Why deterministic-first
The rule layer (hand-crafted Bangla/Banglish/English crime-rule patterns) is primary, and the trained classifier only fills gaps with calibrated thresholds. In legal work, recall on the rule layer is auditable line-by-line — a black box isn't.
Links
- Live: rudra496.github.io/adalatai
- Source: github.com/rudra496/adalatai
If you're working on legal tech for South Asia — or on making legal AI auditable — I'd love to compare notes.
來源：DEV Community · dev.to