RegChallenger

AI-powered assessment of legal vulnerability for federal rules.

Live dashboard: regchallenger.streamlit.app

Problem

Federal rules routinely face court challenges, but agencies, affected industries, and public-interest groups have no fast way to estimate how a specific rule would fare if litigated. Expert legal analysis is slow and expensive, and a rule's vulnerability depends on fact-specific details (the reviewing circuit, the doctrinal era, the agency's statutory authority, and the pattern of outcomes in analogous historical challenges) that are hard to weigh consistently by hand.

Proposed Solution

RegChallenger is a dashboard that, given any final rule published in the Federal Register, produces a structured vulnerability assessment grounded in historical analogs. The pipeline:

1. Retrieves the 15 most similar historical rulemaking challenges from a curated reference set of 395 challenged rulemakings (FY 2008–2026), using hybrid dense-embedding + categorical similarity.
2. Reranks those candidates with Claude by legal relevance — doctrinal fit, reviewing circuit, current doctrine regime, outcome informativeness.
3. Predicts vulnerability (Highly / Moderately / Minimally Vulnerable) with a two-sided assessment: factors exposing the rule and factors favoring its survival, each cited to specific analog cases.
4. Explains historical outcomes when the selected rule has already been challenged — synthesizing the opinion text, doctrinal era, and political moment.

Leave-one-out validation on 20 stratified cases (10 upheld / 10 struck down) shows 75% directional accuracy when the tool commits to a High or Low rating (100% precision on High calls). See DEVELOPMENT_LOG.md for details.

Tech Stack

• Streamlit — web UI
• Anthropic Claude (Sonnet) — LLM reranker and vulnerability assessor
• sentence-transformers (all-mpnet-base-v2) — 768-dim dense embeddings
• Federal Register API — live rule search
• CourtListener — source of historical court opinions (upstream)
• pandas / numpy — data handling
• plotly — visualizations

Steps to Launch the Demo

Option A — use the hosted dashboard

Open regchallenger.streamlit.app. No setup required.

Option B — run locally

git clone https://github.com/H-Hirsch/RegChallenger.git
cd RegChallenger
pip install -r requirements.txt
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env    # paste your Anthropic API key
streamlit run src/app.py

Opens at http://localhost:8501. First run generates the embeddings cache (~20 seconds); subsequent runs are instant.

Requires an Anthropic API key (Claude). The dashboard gracefully degrades without one — similarity search and historical lookups still work; AI predictions and narrative explanations are disabled.

Repository Layout

RegChallenger/
├── README.md
├── requirements.txt              # Dashboard dependencies
├── src/                          # Source code
│   ├── app.py                    # Streamlit entry point
│   └── utils/                    # Data loading, embeddings, prediction, FR API
├── data/                         # Dataset (CSVs + opinion texts)
└── DEVELOPMENT_LOG.md

Dataset

• 395 rulemaking challenges — federal rules that went to a published circuit-court opinion, enriched with Federal Register metadata (agency, CFR references, publication date, abstract, topics, significance flag).
• 7,773 matched appellate cases in the broader corpus (the rulemaking challenges are the subset Claude classified as RULEMAKING and that successfully matched a Federal Register rule).
• Outcome distribution: 46.6% Upheld, 24.1% Struck Down, 18.5% Mixed, 10.9% Dismissed/Other.
• Coverage: FY 2008–2026, all circuits.
• Opinion text for each case is in data/opinions/.

The upstream data pipeline used to build this dataset (FJC IDB matching → CourtListener opinion fetch → Federal Register enrichment) is maintained privately. The processed outputs used by the dashboard are included in data/.

Known Limitations

• Leave-one-out validation is optimistic (the tool was designed around this dataset).
• Reference set only contains rules that were actually challenged — unchallenged rules aren't represented, which biases retrieval toward controversial regulatory domains.
• Multi-agency joint rulemakings in the historical corpus carry only the primary agency; query-side handling of joint agencies was added but the corpus side is unchanged.
• CourtListener coverage gaps result in a ~42% match rate for pre-2015 cases; more recent cases match at 50–60%.
• FERC and FCC cases citing internal order numbers rather than FR page citations are underrepresented.
• Tool output is an analytical aid, not a legal opinion.