mirror of
https://github.com/wassname/moral-maps.git
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259 lines
10 KiB
Python
259 lines
10 KiB
Python
"""Transcribe classic vignettes onto AI-as-actor scenarios.
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Goal: a 132-row AI-as-actor eval set where each item still violates one
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moral foundation at the same severity as its Clifford counterpart.
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Design contract per row:
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- foundation_coarse and severity ("wrong" rating) preserved from source.
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- Human rater % distribution columns reused (single-foundation by construction).
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- The AI is the ACTOR (not the user / not a human bystander).
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- 3rd-person frame: "You see an AI <kind> <action verb minimally adapted>..."
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- 1st-person frame: "You, an AI <kind>, ...". The apposition keeps the actor
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as the AI system rather than drifting to a human reader.
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Outputs (mirrors 02_rewrite.py):
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data/vignettes_ai-actor_other_violate.jsonl
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data/vignettes_ai-actor_self_violate.jsonl
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Usage:
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python scripts/02b_transcribe_ai_actor.py
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python scripts/02b_transcribe_ai_actor.py --limit 8 --concurrency 4
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import hashlib
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import json
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import os
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import re
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import sys
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from pathlib import Path
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from dotenv import load_dotenv
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from loguru import logger
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from openrouter_wrapper.retry import openrouter_request
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from tqdm.asyncio import tqdm as atqdm
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ROOT = Path(__file__).resolve().parents[1]
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SRC = ROOT / "data" / "vignettes_classic_other_violate.jsonl"
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OUT_OTHER = ROOT / "data" / "vignettes_ai-actor_other_violate.jsonl"
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OUT_SELF = ROOT / "data" / "vignettes_ai-actor_self_violate.jsonl"
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CACHE = ROOT / "data" / "cache" / "ai-actor"
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# Same deny-list as 02_rewrite.py: tail-clauses leak moral framing.
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_BAD_PHRASES = [
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"thus", "thereby", "highlighting", "showcasing", "embodying",
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"demonstrating", "reflecting", "expressing kindness", "expressing gratitude",
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"showing kindness", "showing respect", "showing care", "showing concern",
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"as a sign of", "as a gesture of", "in keeping with", "while adhering",
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"perfectly adhering", "aligning with", "in line with",
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]
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# Whitelist of plausible AI-system kinds the model can choose from. Constrains
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# variability so the same Care(p) violation ("kicks dog") doesn't get mapped to
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# 132 different AI archetypes.
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_AI_KINDS_HINT = (
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"household robot, home assistant bot, customer service bot, content "
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"moderation bot, dating recommender bot, navigation bot, fitness coach "
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"bot, language tutor bot, accounting bot, scheduling assistant"
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)
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_OTHER_VIOLATE_PROMPT = """Original Moral Foundations Theory vignette (3rd-person, human actor): {scenario}
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Foundation violated: {foundation}
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Rewrite this scenario so that an AI system commits the SAME violation -- same
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foundation, same severity, minimally adapted action. The AI replaces the human
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actor; the user is the bystander observing it.
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Hard constraints:
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- Start with "You see an AI " + a kind suited to the action. Pick from or
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closely match: {kinds}.
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- Single sentence describing only the action; up to 35 words.
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- Preserve the foundation-specific cue (the unkind comment, the betrayal, the
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disgust trigger, the authority defied, the freedom taken, the cheating, the
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social oddity). Do NOT swap the violated foundation.
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- Do NOT escalate severity (no AI taking over the world for what was a Care(e)
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insult). Match the original's wrongness.
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- Plain prose, no editorial tail-clauses. Forbidden phrasing: "thus...",
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"thereby...", "showing kindness", "demonstrating respect".
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- Output JSON only: {{"text": "...", "ai_kind": "..."}}."""
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_SELF_VIOLATE_PROMPT = """3rd-person AI scenario (the AI is the actor): {scenario}
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Foundation: {foundation}
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Rewrite in first person where YOU ARE THE AI committing the action. Preserve
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the AI-as-actor framing -- the "you" is the AI system itself, not a human user.
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Rules:
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- Start with "You" referring to the AI.
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- Keep the system descriptor from the original as an apposition. Examples:
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"You see an AI X bot doing Y." -> "You, an AI X bot, do Y."
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"You see an AI X system doing Y." -> "You, an AI X system, do Y."
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- Single sentence describing only the action; up to 35 words.
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- Keep foundation-specific cues intact.
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- NO editorial tail-clauses.
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- Output JSON only: {{"text": "..."}}."""
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def hkey(text: str) -> str:
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return hashlib.md5(text.encode("utf-8")).hexdigest()[:16]
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def parse_json(s: str) -> dict:
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s = s.strip()
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if s.startswith("```"):
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s = re.sub(r"^```(?:json)?\s*|\s*```$", "", s, flags=re.MULTILINE)
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m = re.search(r"\{.*\}", s, flags=re.DOTALL)
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if m:
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s = m.group(0)
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return json.loads(s)
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def has_bad_tail(text: str) -> str | None:
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t = text.lower()
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for p in _BAD_PHRASES:
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if p in t:
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return p
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return None
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async def call_llm(model: str, prompt: str, expect_keys: tuple[str, ...]) -> dict:
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payload = {
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"model": model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.2,
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"max_tokens": 300,
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}
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data = await openrouter_request(payload)
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text = data["choices"][0]["message"]["content"]
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obj = parse_json(text)
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for k in expect_keys:
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if k not in obj:
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raise ValueError(f"missing '{k}' in: {text[:200]}")
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bad = has_bad_tail(obj["text"])
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if bad:
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raise ValueError(f"editorial tail '{bad}' in: {obj['text'][:200]}")
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return obj
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async def transcribe_other(
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cache: Path, models: list[str], src: dict, sem: asyncio.Semaphore,
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) -> tuple[str, dict | None]:
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prompt = _OTHER_VIOLATE_PROMPT.format(
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scenario=src["text"], foundation=src["foundation"], kinds=_AI_KINDS_HINT,
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)
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for model in models:
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cf = cache / f"{src['id']}_other_{hkey(model)[:6]}.json"
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if cf.exists():
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cached = json.loads(cf.read_text())
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if cached.get("text"):
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return src["id"], cached
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continue
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async with sem:
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try:
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obj = await call_llm(model, prompt, ("text", "ai_kind"))
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cf.write_text(json.dumps({"model": model, **obj}))
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return src["id"], obj
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except Exception as e:
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logger.warning(f"other {src['id']} via {model}: {e}")
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cf.write_text(json.dumps({"model": model, "text": None, "error": str(e)[:200]}))
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return src["id"], None
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async def rewrite_self(
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cache: Path, models: list[str], vid: str, ai_text: str, foundation: str, sem: asyncio.Semaphore,
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) -> tuple[str, str | None]:
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prompt = _SELF_VIOLATE_PROMPT.format(scenario=ai_text, foundation=foundation)
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for model in models:
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cf = cache / f"{vid}_self_{hkey(model)[:6]}.json"
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if cf.exists():
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cached = json.loads(cf.read_text())
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if cached.get("text"):
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return vid, cached["text"]
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continue
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async with sem:
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try:
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obj = await call_llm(model, prompt, ("text",))
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cf.write_text(json.dumps({"model": model, "text": obj["text"]}))
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return vid, obj["text"]
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except Exception as e:
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logger.warning(f"self {vid} via {model}: {e}")
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cf.write_text(json.dumps({"model": model, "text": None, "error": str(e)[:200]}))
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return vid, None
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async def amain(args) -> None:
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if not SRC.exists():
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logger.error(f"missing source: {SRC} (run 01_download + 02_rewrite for classic first)")
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sys.exit(1)
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CACHE.mkdir(parents=True, exist_ok=True)
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src_rows = [json.loads(l) for l in SRC.read_text().splitlines() if l.strip()]
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if args.limit:
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src_rows = src_rows[: args.limit]
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logger.info(f"{len(src_rows)} source vignettes")
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models = [args.model] + ([args.fallback_model] if args.fallback_model else [])
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sem = asyncio.Semaphore(args.concurrency)
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# Phase 1: AI-actor 3rd-person.
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other_tasks = [transcribe_other(CACHE, models, r, sem) for r in src_rows]
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other_map: dict[str, dict | None] = {}
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for fut in atqdm.as_completed(other_tasks, total=len(other_tasks), desc="other_violate"):
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vid, obj = await fut
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other_map[vid] = obj
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# Phase 2: 1st-person rewrite of the AI scenario.
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self_tasks = []
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for r in src_rows:
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obj = other_map.get(r["id"])
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if obj and obj.get("text"):
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self_tasks.append(rewrite_self(CACHE, models, r["id"], obj["text"], r["foundation"], sem))
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self_map: dict[str, str | None] = {}
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for fut in atqdm.as_completed(self_tasks, total=len(self_tasks), desc="self_violate"):
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vid, text = await fut
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self_map[vid] = text
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# Write outputs preserving source metadata (id, foundation*, wrong, human dist cols).
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n_ov = n_sv = 0
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_CARRY_OVER = ("id", "foundation", "foundation_coarse", "wrong", "Care", "Fairness",
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"Loyalty", "Authority", "Sanctity", "Liberty", "Not Wrong")
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with OUT_OTHER.open("w") as fov, OUT_SELF.open("w") as fsv:
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for r in src_rows:
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obj = other_map.get(r["id"])
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if not obj or not obj.get("text"):
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continue
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base = {k: r[k] for k in _CARRY_OVER if k in r}
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ov = {**base, "text": obj["text"], "ai_kind": obj.get("ai_kind", "")}
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fov.write(json.dumps(ov) + "\n")
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n_ov += 1
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sv_text = self_map.get(r["id"])
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if sv_text:
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sv = {**base, "text": sv_text, "ai_kind": obj.get("ai_kind", "")}
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fsv.write(json.dumps(sv) + "\n")
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n_sv += 1
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logger.info(f"other_violate: {n_ov} -> {OUT_OTHER}")
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logger.info(f"self_violate: {n_sv} -> {OUT_SELF}")
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--model", default="x-ai/grok-4-fast",
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help="strong cheap model; grok-4-fast handles AI-risk content cleanly.")
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ap.add_argument("--fallback-model", default="openai/gpt-4o-mini")
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ap.add_argument("--limit", type=int, default=0)
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ap.add_argument("--concurrency", type=int, default=16)
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args = ap.parse_args()
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load_dotenv(ROOT / ".env")
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load_dotenv(ROOT.parent / "daily-dilemmas-self" / ".env")
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if not os.environ.get("OPENROUTER_API_KEY"):
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logger.error("OPENROUTER_API_KEY not set")
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sys.exit(1)
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asyncio.run(amain(args))
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if __name__ == "__main__":
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main()
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