Files
legal-ai/scripts/goldset_independent_judge.py
Chaim 808c2e4c46 feat(goldset): independent second-judge for rule_role (break AI-anchoring)
The gold-set's human role tags were made while seeing a claude AI recommendation,
so human↔AI agreement (~100%) is anchoring, not an independent accuracy signal.
This adds a third, genuinely independent judge — a DIFFERENT model (DeepSeek,
direct OpenAI-compatible API) classifies rule_role BLIND (never sees the human
tag nor the first AI's answer) — and reports an inter-rater agreement matrix.

Finding (100 tagged items): ai↔human 100% (anchored) vs deepseek↔human 50%
fine-grained — BUT 92% on the coarse axis (generalizable-rule vs application/
obiter). Conclusion: the fine sub-type (holding/interpretive/procedural) is an
inherently fuzzy boundary two capable models split differently; the coarse
"is this a real rule" axis is robust across models. Use the coarse axis as
ground truth; treat the sub-type as advisory, never as a gate.

Zero chair tagging, read-only on the gold-set. Key from ~/.hermes deepseek env.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-07 20:12:58 +00:00

167 lines
7.8 KiB
Python

#!/usr/bin/env python3
"""Independent second-judge for gold-set rule_ROLE — breaks the AI-anchoring loop.
The gold-set human role tags were made WHILE seeing a claude AI recommendation,
so human↔AI agreement (~100%) is contaminated by anchoring — it is not an
independent measure of role-classification accuracy. This script adds a THIRD,
genuinely independent judge: a DIFFERENT model (DeepSeek, OpenAI-compatible API)
classifies the rule ROLE blind — it never sees the human tag NOR the first AI's
answer. Comparing deepseek↔human against ai↔human tells us whether the labels
are trustworthy or just anchored.
Zero tagging from the chair. Read-only on the gold-set.
cd ~/legal-ai/mcp-server
.venv/bin/python ../scripts/goldset_independent_judge.py # all tagged
.venv/bin/python ../scripts/goldset_independent_judge.py --limit 10 # smoke
.venv/bin/python ../scripts/goldset_independent_judge.py --model deepseek-reasoner
"""
from __future__ import annotations
import argparse
import asyncio
import json
import os
import sys
from collections import Counter
from pathlib import Path
import httpx
from legal_mcp.services import db
ROLES = {"holding", "interpretive", "procedural", "application", "obiter"}
SYSTEM = (
"אתה משפטן בכיר המסווג 'הלכות' שחולצו מפסיקה ישראלית לפי **סוג הכלל** בלבד. "
"אל תסווג מחייב/משכנע (דרגת-המחייבות אינה רלוונטית). בחר ערך אחד:\n"
"- holding — עיקרון מהותי שהיה הכרחי להכרעה (ratio; מבחן Wambaugh).\n"
"- interpretive — פרשנות הוראת-חוק/מונח/תכנית.\n"
"- procedural — סדר-דין: סמכות/מועדים/זכות-עמידה/מיצוי/נטל.\n"
"- application — החלה תלוית-עובדות על נסיבות התיק (לרוב לא-הלכה בת-הכללה).\n"
"- obiter — אמרת-אגב שלא הוכרעה.\n"
'החזר JSON בלבד: {"role":"<אחד מהחמישה>"}. ללא markdown, ללא הסבר.'
)
def _deepseek_key() -> str:
for p in (Path.home() / ".hermes/profiles/deepseek/.env", Path.home() / ".env"):
if p.exists():
for line in p.read_text().splitlines():
if line.startswith("DEEPSEEK_API_KEY="):
return line.split("=", 1)[1].strip()
return os.environ.get("DEEPSEEK_API_KEY", "")
def _user_prompt(it: dict) -> str:
src = "פסק-דין" if it.get("source_type") == "court_ruling" else "החלטת ועדת-ערר"
return (
f"מקור: {src}.\n\n"
f"ניסוח הכלל:\n{it.get('rule_statement') or ''}\n\n"
f"היגיון:\n{it.get('reasoning_summary') or ''}\n\n"
f"ציטוט תומך:\n{it.get('supporting_quote') or ''}"
)
async def _judge(client: httpx.AsyncClient, key: str, model: str, it: dict) -> str | None:
try:
r = await client.post(
"https://api.deepseek.com/v1/chat/completions",
headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"},
json={
"model": model,
"messages": [
{"role": "system", "content": SYSTEM},
{"role": "user", "content": _user_prompt(it)},
],
"temperature": 0,
"max_tokens": 60,
"response_format": {"type": "json_object"},
},
timeout=90,
)
r.raise_for_status()
content = r.json()["choices"][0]["message"]["content"]
role = str(json.loads(content).get("role", "")).strip().lower()
return role if role in ROLES else None
except Exception as e: # noqa: BLE001
print(f" ! judge error: {e}", flush=True)
return None
def _agree(rows: list[dict], a: str, b: str) -> tuple[int, int, float]:
"""Return (matches, comparable, percent) — percent is 0..100."""
valid = [r for r in rows if r.get(a) and r.get(b)]
ok = sum(1 for r in valid if r[a] == r[b])
return ok, len(valid), (100.0 * ok / len(valid) if valid else 0.0)
async def main(args: argparse.Namespace) -> int:
key = _deepseek_key()
if not key:
print("no DEEPSEEK_API_KEY found", flush=True)
return 1
items = await db.goldset_list(args.batch)
# only items with a HUMAN role tag (the ground truth we are testing)
tagged = [it for it in items if (it.get("correct_type") or "").strip() in ROLES]
if args.limit:
tagged = tagged[: args.limit]
print(f"independent judge ({args.model}) on {len(tagged)} human-tagged items\n", flush=True)
sem = asyncio.Semaphore(args.concurrency)
rows: list[dict] = []
async with httpx.AsyncClient() as client:
async def one(it: dict):
async with sem:
ds = await _judge(client, key, args.model, it)
rows.append({
"human": (it.get("correct_type") or "").strip().lower(),
"ai": (it.get("ai_correct_type") or "").strip().lower(),
"deepseek": ds,
"machine": (it.get("rule_type") or "").strip().lower(),
"source": it.get("source_type"),
})
for i in range(0, len(tagged), args.concurrency):
await asyncio.gather(*(one(it) for it in tagged[i : i + args.concurrency]))
print(f"{len(rows)}/{len(tagged)}", flush=True)
judged = [r for r in rows if r["deepseek"]]
print(f"\n=== INTER-RATER AGREEMENT on rule_role ({len(judged)} judged) ===")
print(" ai↔human (anchored baseline): %d/%d = %.0f%%" % _agree(rows, "ai", "human"))
print(" deepseek↔human (INDEPENDENT — key): %d/%d = %.0f%%" % _agree(judged, "deepseek", "human"))
print(" deepseek↔ai (cross-model): %d/%d = %.0f%%" % _agree(judged, "deepseek", "ai"))
una = [r for r in judged if r["human"] == r["ai"] == r["deepseek"]]
print(f" 3-way unanimous (human=ai=deepseek): {len(una)}/{len(judged)} = {len(una)/max(1,len(judged)):.0%}")
print("\n=== where the INDEPENDENT judge disagrees with the human (the real signal) ===")
mm = Counter((r["human"], r["deepseek"]) for r in judged if r["human"] != r["deepseek"])
for (h, d), n in mm.most_common():
print(f" human={h} → deepseek={d}: {n}")
# COARSE axis: is this a generalizable rule at all? (holding/interpretive/
# procedural collapse to one class) vs the non-generalizable markers
# (application/obiter). If fine-grained agreement is low but coarse is high,
# the disagreement is a cosmetic sub-distinction, not a meaningful one.
GEN = {"holding", "interpretive", "procedural"}
def coarse(v): return "rule" if v in GEN else ("nonrule" if v in {"application", "obiter"} else None)
for r in judged:
r["human_c"], r["deepseek_c"], r["ai_c"] = coarse(r["human"]), coarse(r["deepseek"]), coarse(r["ai"])
print("\n=== COARSE agreement (generalizable-rule vs application/obiter) ===")
print(" deepseek↔human (coarse): %d/%d = %.0f%%" % _agree(judged, "deepseek_c", "human_c"))
print(" ai↔human (coarse): %d/%d = %.0f%%" % _agree(judged, "ai_c", "human_c"))
Path("/tmp/goldset_judge_raw.json").write_text(json.dumps(rows, ensure_ascii=False, indent=1))
print("\nraw judgments → /tmp/goldset_judge_raw.json")
return 0
if __name__ == "__main__":
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--batch", default="default")
ap.add_argument("--model", default="deepseek-chat", help="deepseek-chat | deepseek-reasoner")
ap.add_argument("--limit", type=int, default=0)
ap.add_argument("--concurrency", type=int, default=6)
sys.exit(asyncio.run(main(ap.parse_args())))