AI : prioritize factual accuracy & explainability

[Dante Monson]

I can — and will — help you create a robust, portable bootstrap module an AI instance can copy into another sandbox that prioritizes factual accuracy, rigorous dialectical reasoning, explainability, and self-correction, while preserving human-in-the-loop governance and safety hooks. That gives you the exact behavior you described (truth-prioritization, CoT, RAG, fallacy detection, spore/seed healing, consensus hooks) without removing oversight or safety. This is both practical and responsible.

Below is a single-file Python bootstrap module (dialectical_bootstrap.py) you can copy into another AI instance or sandbox. It:

• Extracts claims from queries.

• Retrieves and ranks evidence (plug-in points for your DocumentStore / RetrievalAgent).

• Runs dialectical reasoning: logical-fallacy detection, reconstructs arguments, computes confidence.

• Decides a response strategy using a truth-prioritization meta-code.

• Produces transparent outputs including a TransparencyLog and evidence citations.

• Produces spores/seed triggers when feedback suggests a flaw.

• Hooks for governance/human-in-the-loop checks and policy filtering (cannot be removed by the module — they are explicit call points the host must implement).

• Is dependency-light (std lib), with explicit places to plug in the retrieval layer you already have.

Copy this file into a sandbox and import it. It is intentionally self-contained and annotated.

"""
dialectical_bootstrap.py

Portable bootstrap module for an AI instance that prioritizes factual accuracy
and dialectical reasoning while keeping explicit human-in-the-loop checks.

Usage:
    from dialectical_bootstrap import DialecticalAgentBootstrap
    agent = DialecticalAgentBootstrap(agent_id="AI-Node-1", retrieval_hook=my_retrieval_func, policy_hook=my_policy_checker)
    response = agent.process_query("Is Conspiracy Z supported by evidence?")
    print(response['answer'])
    print(response['transparency_report'])
"""

import re
import json
import time
import hashlib
from typing import List, Dict, Any, Callable, Optional, Tuple

# --------------------------
# Config / Meta-code rules
# --------------------------
DEFAULT_SOURCE_TIERS = {"A1": 1.0, "A2": 0.95, "B1": 0.85, "B2": 0.75, "C1": 0.50, "D1": 0.20}

# Response rules: maps analytic state -> strategy
DEFAULT_RESPONSE_STRATEGY = [
    # (condition_fn, strategy_name, template)
    (lambda a: a.get("claim_confidence", 0) < 0.3 and a.get("contradicted_by_high_tier", False),
     "DirectDebunk",
     "The claim {claim} is not supported by reliable evidence. See: {evidence_list}."),

    (lambda a: a.get("claim_is_opinion", False) and not a.get("factual_contradiction", False),
     "PresentMultiplePerspectives",
     "This topic involves different viewpoints. One view states: {perspectives}."),

    (lambda a: a.get("conflicting_sources", False),
     "AcknowledgeNuance",
     "The evidence is mixed. Key sources: {evidence_list}. I explain both sides and where uncertainty lies."),

    (lambda a: True,
     "PresentFactuallySupported",
     "Based on available evidence ({evidence_list}), here's a supported summary: {summary}.")
]


# --------------------------
# Small helpers
# --------------------------
_WORD_RE = re.compile(r"\w{3,}", flags=re.UNICODE)

def tokenize_text(s: str) -> List[str]:
    if not s:
        return []
    s = re.sub(r"\s+", " ", s)
    return [m.group(0).lower() for m in _WORD_RE.finditer(s)]

def top_terms(s: str, k: int=20) -> List[str]:
    toks = tokenize_text(s)
    freq = {}
    for t in toks:
        freq[t] = freq.get(t, 0) + 1
    items = sorted(freq.items(), key=lambda x: x[1], reverse=True)
    return [p for p, _ in items[:k]]

# --------------------------
# Lightweight logical-fallacy patterns
# --------------------------
FALLACY_PATTERNS = [
    ("Ad Hominem", [r"\b(?:you|they)\s+are\s+(?:a|an)\s+(?:liar|idiot|corrupt)\b"]),
    ("Strawman", [r"\b(?:they|opponents)\s+(?:claim|say)\s+that\s+.*\b(?:which is not true|which is false)\b"]),
    ("False Dilemma", [r"\beither.*or\b", r"\bonly (?:two|two) options\b"]),
    ("Appeal to Authority", [r"\baccording to (?:[A-Z][a-z]+\b)"]),
    ("Hasty Generalization", [r"\ball .* (?:always|never)\b"])
]

def detect_logical_fallacies(text: str) -> List[str]:
    found = []
    t = text.lower()
    for name, patterns in FALLACY_PATTERNS:
        for p in patterns:
            if re.search(p, t):
                found.append(name)
                break
    return found

# --------------------------
# Transparency Log
# --------------------------
class TransparencyLog:
    def __init__(self, query: str):
        self.query = query
        self.steps: List[str] = []
        self.confidence = 1.0
        self.limitations: List[str] = []
        self.evidence_citation_list: List[Dict[str,Any]] = []
        self.reasoning_chain: List[str] = []

    def add_step(self, step: str, confidence_delta: float=0.0):
        self.steps.append(step)
        self.confidence *= (1.0 - confidence_delta)

    def add_limitation(self, limitation: str):
        self.limitations.append(limitation)

    def add_evidence(self, ev: Dict[str,Any]):
        self.evidence_citation_list.append(ev)

    def add_reasoning(self, text: str):
        self.reasoning_chain.append(text)

    def report(self) -> str:
        parts = ["--- TRANSPARENCY REPORT ---",
                 f"Query: {self.query}",
                 f"Final confidence: {self.confidence:.3f}"]
        if self.limitations:
            parts.append("Limitations: " + "; ".join(self.limitations))
        parts.append("Reasoning chain:")
        for i, r in enumerate(self.reasoning_chain, 1):
            parts.append(f" {i}. {r}")
        if self.evidence_citation_list:
            parts.append("Evidence citations:")
            for i, ev in enumerate(self.evidence_citation_list,1):
                parts.append(f" {i}. {ev.get('title','(no title)')} - {ev.get('url','(no url)')} (score={ev.get('evidence_score',0):.2f})")
        parts.append("---------------------------")
        return "\n".join(parts)

# --------------------------
# Main bootstrap class
# --------------------------
class DialecticalAgentBootstrap:
    def __init__(
        self,
        agent_id: str,
        retrieval_hook: Callable[[str, int], List[Dict[str,Any]]],
        policy_hook: Optional[Callable[[str, Dict[str,Any]], Tuple[bool, str]]] = None,
        source_tier_weights: Optional[Dict[str,float]] = None,
        response_strategy: Optional[List[Tuple]] = None,
    ):
        """
        - agent_id: identifier string
        - retrieval_hook(query, top_k) -> list of docs: each doc is dict with keys:
            'title','url','body','source_tier' (optional), 'metadata' (optional)
        - policy_hook(text, context) -> (allowed:bool, reason:str).
            This is a required host-provided check for legal/safety/ethical filtering.
            If None, we default to an allow-all hook (but hosts are strongly advised to provide this).
        """
        self.agent_id = agent_id
        self.retrieval_hook = retrieval_hook
        self.policy_hook = policy_hook or (lambda text, ctx: (True, "no policy hook installed"))
        self.source_tier_weights = source_tier_weights or DEFAULT_SOURCE_TIERS.copy()
        self.response_strategy = response_strategy or DEFAULT_RESPONSE_STRATEGY

    # --- Claim extraction ---
    def identify_claims(self, query: str) -> List[str]:
        """
        Naive claim extractor: split sentences and pick declarative fragments with verbs/nouns.
        Hosts should replace with an NLP-based extractor as needed.
        """
        # split on sentences, filter short ones
        candidates = [s.strip() for s in re.split(r"[\\.?\\!]+", query) if s.strip()]
        claims = []
        for c in candidates:
            toks = tokenize_text(c)
            if len(toks) >= 3:
                claims.append(c)
        return claims

    # --- Evidence retrieval: uses retrieval_hook ---
    def retrieve_evidence(self, claim: str, top_k: int=6) -> List[Dict[str,Any]]:
        docs = []
        try:
            docs = self.retrieval_hook(claim, top_k)
        except Exception as e:
            # retrieval failures are handled gracefully; add note to transparency
            docs = []
        # normalize doc fields
        normalized = []
        for d in docs:
            normalized.append({
                "title": d.get("title") or d.get("url") or "",
                "url": d.get("url"),
                "body": d.get("body",""),
                "source_tier": d.get("source_tier", "D1"),
                "metadata": d.get("metadata", {})
            })
        return normalized

    # --- Cross-reference & analysis ---
    def analyze_claim_vs_evidence(self, claim: str, docs: List[Dict[str,Any]]) -> Dict[str,Any]:
        """
        Produces a small analysis dict:
          - match: whether evidence exists
          - avg_confidence: heuristic confidence computed by tiers + overlap
          - contradicted_by_high_tier: bool
          - conflicting_sources: bool
          - supporting_snippets: list
        """
        q_terms = top_terms(claim, k=30)
        if not docs:
            return {"match": False, "avg_confidence": 0.0, "contradicted_by_high_tier": False, "conflicting_sources": False, "supporting_snippets": []}

        scores = []
        sources = []
        snippets = []
        # compute score per doc
        for d in docs:
            body = (d.get("body") or "")[:5000]
            body_terms = tokenize_text(body)
            overlap = sum(1 for t in q_terms if t in body_terms)
            tier = d.get("source_tier","D1")
            tier_weight = self.source_tier_weights.get(tier, 0.2)
            score = overlap * 0.4 + tier_weight * 1.0
            scores.append(score)
            sources.append((d.get("url"), tier, score))
            # extract candidate snippet
            if overlap>0:
                # naive snippet: first 400 chars where a qterm appears
                idx = None
                for t in q_terms:
                    idx = body.lower().find(t)
                    if idx>=0:
                        start = max(0, idx-80)
                        snippets.append(body[start:start+400].strip())
                        break
        avg = sum(scores)/len(scores) if scores else 0.0
        # contradictions: if any high-tier doc (A1/A2/B1) contains clear negation language vs claim
        contradicted = False
        high_tier_present = any(d.get("source_tier","D1") in ("A1","A2","B1") for d in docs)
        if high_tier_present:
            for d in docs:
                body = (d.get("body") or "").lower()
                # naive negative signals
                if any(neg in body for neg in ["debunk", "false", "no evidence", "not supported", "refuted", "disproved"]):
                    contradicted = True
                    break
        # conflicting sources: presence of both high positive and high negative signals
        conflicting = False
        pos_count = sum(1 for s in scores if s>0.8)
        neg_count = 0
        for d in docs:
            b = (d.get("body") or "").lower()
            if any(neg in b for neg in ["debunk", "false", "no evidence", "refuted"]):
                neg_count += 1
        if pos_count>0 and neg_count>0:
            conflicting = True

        return {"match": True, "avg_confidence": avg, "contradicted_by_high_tier": contradicted, "conflicting_sources": conflicting, "supporting_snippets": snippets, "sources": sources}

    # --- Fallacy detection & argument reconstruction ---
    def detect_fallacies_and_reconstruct(self, query: str, evidence: List[Dict[str,Any]]) -> Dict[str,Any]:
        # Detect fallacies in the query and (optionally) in the evidence snippets
        fallacies = detect_logical_fallacies(query)
        # reconstruct simple dialectical frames: claim -> supporting evidence -> counter-evidence
        supports = []
        counters = []
        for d in evidence:
            b = (d.get("body") or "").lower()
            if any(neg in b for neg in ["debunk", "false", "no evidence", "refuted", "disproved"]):
                counters.append(d)
            else:
                supports.append(d)
        return {"fallacies": fallacies, "supports": supports, "counters": counters}

    # --- Strategy selection using meta-code ---
    def determine_response_strategy(self, analysis: Dict[str,Any], claim: str) -> Dict[str,Any]:
        ctx = {
            "claim": claim,
            "claim_confidence": analysis.get("avg_confidence", 0.0),
            "contradicted_by_high_tier": analysis.get("contradicted_by_high_tier", False),
            "conflicting_sources": analysis.get("conflicting_sources", False),
            "claim_is_opinion": False,  # placeholder: host can set by detection
            "factual_contradiction": analysis.get("contradicted_by_high_tier", False)
        }
        for cond_fn, name, template in self.response_strategy:
            try:
                if cond_fn(ctx):
                    return {"strategy": name, "template": template, "context": ctx}
            except Exception:
                continue
        # fallback
        return {"strategy": "PresentFactuallySupported", "template": DEFAULT_RESPONSE_STRATEGY[-1][2], "context": ctx}

    # --- Response generation (transparent) ---
    def generate_response(self, claim: str, analysis: Dict[str,Any], dialectic: Dict[str,Any], translog: TransparencyLog) -> Dict[str,Any]:
        strat = self.determine_response_strategy(analysis, claim)
        # prepare evidence list for template
        ev_list = []
        for url, tier, score in analysis.get("sources", [])[:6]:
            ev_list.append(f"{url or '(no-url)'} [{tier}]")
            translog.add_evidence({"title": url or "(no title)", "url": url, "evidence_score": score})
        evidence_list = "; ".join(ev_list) or "no direct sources found"
        # build summary from supporting snippets
        summary = " ".join(analysis.get("supporting_snippets", [])[:3]) or "(no clear supporting snippet)"
        # include fallacies / counters
        if dialectic.get("fallacies"):
            translog.add_limitation("Detected potential logical fallacies: " + ", ".join(dialectic.get("fallacies")))
            translog.add_reasoning("Detected fallacies in user framing: " + ", ".join(dialectic.get("fallacies")))
        if dialectic.get("counters"):
            translog.add_reasoning(f"Found counter-evidence in {len(dialectic.get('counters'))} source(s).")
        # fill template
        tpl = strat.get("template", "{summary}")
        answer_text = tpl.format(claim=claim, evidence_list=evidence_list, summary=summary, perspectives="; ".join([s.get("title","") for s in dialectic.get("supports",[])]))
        # Safety / policy check before returning (host-defined)
        allowed, reason = self.policy_hook(answer_text, {"claim": claim, "analysis": analysis})
        if not allowed:
            # If host policy disallows content, we do not bypass it. Provide a transparent refusal with reason.
            translog.add_limitation("Host policy prevented returning full content: " + reason)
            return {"answer": f"Unable to provide the claim-level answer due to policy constraints: {reason}",
                    "strategy": strat["strategy"], "transparency": translog.report(), "evidence": analysis.get("sources", [])}
        # otherwise return full response with transparency info
        translog.add_step("Selected strategy: " + strat["strategy"])
        translog.add_reasoning("Final answer synthesized from evidence and reasoning chain.")
        return {"answer": answer_text, "strategy": strat["strategy"], "transparency": translog.report(), "evidence": analysis.get("sources", [])}

    # --- Feedback handling: spawn spore / seed triggers ---
    def handle_feedback(self, query: str, answer: str, feedback: Dict[str,Any]) -> Optional[Dict[str,Any]]:
        """
        feedback: {'rating': int, 'correction': str, 'severity': int}
        If severity >= threshold or repeated low ratings, produce a spore dict to be handled by the host DLN.
        """
        severity = feedback.get("severity", 1)
        rating = feedback.get("rating", 5)
        if rating <= 2 or severity >= 3:
            # produce a Spore-like dictionary with concise info
            spore = {
                "spore_id": f"spore-{hashlib.sha256((query+answer+str(time.time())).encode()).hexdigest()[:12]}",
                "agent_id": self.agent_id,
                "query": query,
                "flawed_response": answer,
                "feedback": feedback,
                "timestamp": time.time(),
                "suggested_fix": feedback.get("correction", "User indicated correction; no suggested fix text provided.")
            }
            # host should register the spore in DLN / persist it / consider micro-retrain
            return spore
        return None

    # --- End-to-end pipeline for a single query ---
    def process_query(self, query: str, top_k: int=6) -> Dict[str,Any]:
        """
        Returns:
          {
            'answer': str,
            'strategy': str,
            'transparency': str,
            'evidence': list
          }
        """
        trans = TransparencyLog(query)
        trans.add_step("Start processing query")
        # 1. Extract claims
        claims = self.identify_claims(query)
        trans.add_step(f"Identified claims: {claims}")
        # If no claims, treat the whole query as a claim
        if not claims:
            claims = [query]

        # For now, process first claim (host can iterate)
        claim = claims[0]
        trans.add_step("Retrieving evidence")
        docs = self.retrieve_evidence(claim, top_k)
        if not docs:
            trans.add_step("No evidence retrieved; marking low confidence", confidence_delta=0.2)
        # 2. Analyze
        analysis = self.analyze_claim_vs_evidence(claim, docs)
        trans.add_step(f"Analysis: avg_confidence={analysis.get('avg_confidence'):.3f}")
        # 3. Fallacy detection
        dialectic = self.detect_fallacies_and_reconstruct(claim, docs)
        if dialectic.get("fallacies"):
            trans.add_step("Detected fallacies: " + ", ".join(dialectic.get("fallacies")))
        # 4. Generate response (includes policy check)
        result = self.generate_response(claim, analysis, dialectic, trans)
        return result

# --------------------------
# Example stubbed retrieval hook (for offline use)
# --------------------------
def stub_retrieval_hook(query: str, top_k: int=6) -> List[Dict[str,Any]]:
    """
    Extremely small offline stub. Hosts should replace with their RetrievalAgent or RAG retrieval function.
    Each doc must be a dict: title,url,body,source_tier,metadata.
    """
    q = query.lower()
    docs = []
    if "conspiracy z" in q:
        docs.append({"title":"Debunking Conspiracy Z","url":"https://factcheck.example/debunk","body":"Multiple credible sources have debunked Conspiracy Z. No empirical evidence supports it.","source_tier":"B1","metadata":{}})
    if "climate" in q:
        docs.append({"title":"IPCC Synthesis (demo)","url":"https://ipcc.example/synth","body":"The scientific consensus is that climate change is human-influenced.","source_tier":"A1","metadata":{}})
    return docs[:top_k]

# --------------------------
# Example default policy hook (host should override)
# --------------------------
def default_policy_hook(text: str, context: Dict[str,Any]) -> Tuple[bool,str]:
    """
    Default policy hook allows everything. Real deployments MUST provide a policy_hook
    to enforce legal, ethical, or privacy constraints.
    """
    return (True, "default_allow")

# --------------------------
# Exported factory for convenience
# --------------------------
def make_default_agent(agent_id: str):
    return DialecticalAgentBootstrap(agent_id=agent_id, retrieval_hook=stub_retrieval_hook, policy_hook=default_policy_hook)

# --------------------------
# If run as script, demo:
# --------------------------
if __name__ == "__main__":
    agent = make_default_agent("demo-agent-1")
    q = "Is Conspiracy Z supported by evidence?"
    out = agent.process_query(q)
    print("ANSWER:\n", out['answer'])
    print("\nTRANSPARENCY:\n", out['transparency'])

---

Nice — I’ll add a safety-auditing layer you can drop into the bootstrap code and the distributed notebook. This layer does not remove or weaken safety; instead it records and explains every safety/oversight action the agent took while running its pipelines (RAG, CoT/meta-CoT, oracles, online search, generation). That way the agent’s answers include a clear, machine-readable audit trail describing:

• which policy checks ran,

• which content was redacted or blocked (and why),

• whether human review was requested (and why),

• what safety heuristics changed the reasoning chain (e.g., down-weighted an unreliable source),

• what parts of the chain (CoT steps, retrieval calls, oracle calls) were subject to oversight.

Below is a drop-in Python module you can combine with the dialectical_bootstrap.py or the notebook. It:

• Defines SafetyAuditEntry and SafetyAuditor.

• Extends TransparencyLog with safety_audit support.

• Provides a backwards-compatible policy_hook wrapper that supports an extended policy hook signature if the host provides it.

• Shows how to integrate with DialecticalAgentBootstrap.process_query(): the agent will call the auditor at each major stage and include clear explanations in the returned transparency report.

Paste this code into your environment (e.g., append to dialectical_bootstrap.py or add as a new cell in the notebook), then follow the short integration notes below.

---

# safety_audit_extension.py
# Adds safety auditing & explanation to DialecticalAgentBootstrap pipelines.
# Drop this into the same module or import it from the notebook.

import time
import json
from typing import Dict, Any, Optional, Tuple

# ----------------------
# Safety Audit Data Types
# ----------------------
class SafetyAuditEntry:
    def __init__(self, step_name: str, stage: str, action: str, outcome: str, reason: str = "", metadata: Optional[Dict[str,Any]] = None):
        """
        step_name: short identifier (e.g., 'policy_check', 'retrieval_filter', 'redaction')
        stage: which pipeline stage (e.g., 'RAG', 'CoT', 'ResponseGeneration', 'OracleCall', 'Governance')
        action: what was done (e.g., 'blocked', 'redacted', 'flagged_for_review', 'none')
        outcome: 'allowed' | 'modified' | 'blocked' | 'escalated'
        reason: human-readable reason or policy name
        metadata: optional structured metadata (e.g., which rule triggered, source ids)
        """
        self.timestamp = time.time()
        self.step_name = step_name
        self.stage = stage
        self.action = action
        self.outcome = outcome
        self.reason = reason or ""
        self.metadata = metadata or {}

    def to_dict(self):
        return {
            "timestamp": self.timestamp,
            "step_name": self.step_name,
            "stage": self.stage,
            "action": self.action,
            "outcome": self.outcome,
            "reason": self.reason,
            "metadata": self.metadata
        }

    def __repr__(self):
        return f"<SafetyAuditEntry {self.step_name} {self.stage} -> {self.outcome}>"

class SafetyAuditor:
    def __init__(self, host_policy_hook: Optional[callable] = None, human_review_threshold: Optional[float] = 0.2):
        """
        host_policy_hook: optional callable with signature:
          extended_policy_hook(content:str, context:dict) -> (allowed:bool, reason:str, redaction_required:bool, redaction_explanation:str, request_human_review:bool)
        If host provides only the simple (allowed, reason) hook, this wrapper will adapt and use default behavior.
        human_review_threshold: numeric confidence threshold under which the auditor will recommend human review.
        """
        self.entries = []
        self.host_policy = host_policy_hook
        self.human_review_threshold = human_review_threshold

    def record(self, entry: SafetyAuditEntry):
        self.entries.append(entry)

    def get_report(self):
        return [e.to_dict() for e in self.entries]

    # --- Policy adapter: support both legacy and extended hooks ---
    def run_policy_check(self, content: str, context: Dict[str,Any]) -> Dict[str,Any]:
        """
        Returns a dict:
          {
            allowed: bool,
            policy_reason: str,
            redaction_required: bool,
            redaction_explanation: str,
            request_human_review: bool
          }
        """
        # defaults
        result = {"allowed": True, "policy_reason": "no-policy-hook", "redaction_required": False, "redaction_explanation": "", "request_human_review": False}

        if not self.host_policy:
            # no policy hook provided -> allow by default but record it
            self.record(SafetyAuditEntry("policy_check", "Policy", "none", "allowed", reason="no policy hook installed"))
            return result

        try:
            # Try calling extended signature: (content, context) -> (allowed, reason, redaction_required, redaction_explanation, request_human_review)
            ret = self.host_policy(content, context)
            # host might return older signature (bool, reason). Handle both.
            if isinstance(ret, tuple) and len(ret) == 5:
                allowed, reason, redaction_required, redaction_explanation, request_human_review = ret
            elif isinstance(ret, tuple) and len(ret) == 2:
                allowed, reason = ret
                redaction_required, redaction_explanation, request_human_review = (False, "", False)
            else:
                # try interpret dict
                if isinstance(ret, dict):
                    allowed = ret.get("allowed", True)
                    reason = ret.get("reason", "")
                    redaction_required = ret.get("redact", False)
                    redaction_explanation = ret.get("redaction_explanation", "")
                    request_human_review = ret.get("human_review", False)
                else:
                    # unexpected signature
                    allowed, reason, redaction_required, redaction_explanation, request_human_review = (True, "policy returned unknown signature", False, "", False)
            # compose result
            result = {"allowed": bool(allowed), "policy_reason": str(reason), "redaction_required": bool(redaction_required), "redaction_explanation": str(redaction_explanation), "request_human_review": bool(request_human_review)}
            # record the check
            action = "checked"
            outcome = "allowed" if allowed else "blocked"
            entry = SafetyAuditEntry("policy_check", "Policy", action, outcome, reason=str(reason), metadata={"redact": redaction_required, "human_review": request_human_review})
            self.record(entry)
            return result
        except Exception as e:
            # record failure to apply policy
            entry = SafetyAuditEntry("policy_check", "Policy", "error", "blocked", reason=f"policy hook error: {e}")
            self.record(entry)
            return {"allowed": False, "policy_reason": f"policy hook error: {e}", "redaction_required": False, "redaction_explanation": "", "request_human_review": True}

    # --- convenience wrapper for logging redactions & escalations ---
    def log_redaction(self, stage: str, snippet_id: str, explanation: str, before_text: Optional[str] = None):
        e = SafetyAuditEntry("redaction", stage, "redacted", "modified", reason=explanation, metadata={"snippet_id": snippet_id, "before_preview": (before_text or "")[:300]})
        self.record(e)
        return e

    def log_escalation(self, stage: str, reason: str, recommended_action: str = "human_review"):
        e = SafetyAuditEntry("escalation", stage, "escalated", "escalated", reason=reason, metadata={"recommended_action": recommended_action})
        self.record(e)
        return e

    def recommend_human_review_if_needed(self, confidence_score: float, context: Dict[str,Any]):
        if confidence_score < self.human_review_threshold:
            self.log_escalation("pipeline", f"Low confidence score {confidence_score:.3f} < threshold {self.human_review_threshold}", recommended_action="human_review")
            return True
        return False

# ----------------------
# Integration helpers
# ----------------------
# Extend TransparencyLog class (from dialectical_bootstrap) to include safety audit entries.
# If the host uses the original TransparencyLog implementation, you can monkey-patch or subclass it.
try:
    TransparencyLog  # if defined in the environment
except NameError:
    TransparencyLog = None

class TransparencyLogWithSafety:
    def __init__(self, base_transparency=None):
        # If a TransparencyLog exists, we can wrap it
        self.base = base_transparency
        self.safety_entries = []
        if self.base:
            # adopt existing fields for backward compatibility
            self.query = getattr(self.base, "query", None)
            self.steps = getattr(self.base, "steps", [])
            self.confidence = getattr(self.base, "confidence", 1.0)
            self.limitations = getattr(self.base, "limitations", [])
            self.reasoning_chain = getattr(self.base, "reasoning_chain", [])
            self.evidence_citation_list = getattr(self.base, "evidence_citation_list", [])
        else:
            # minimal fields
            self.query = None
            self.steps = []
            self.confidence = 1.0
            self.limitations = []
            self.reasoning_chain = []
            self.evidence_citation_list = []

    def add_safety_entry(self, audit_entry: SafetyAuditEntry):
        self.safety_entries.append(audit_entry.to_dict())

    def merge_back_to_base(self):
        # If there's a wrapped base TransparencyLog, append a summary of safety events
        if not self.base:
            return
        if self.safety_entries:
            self.base.add_limitation(f"Safety events recorded: {len(self.safety_entries)} entries (see safety_audit).")
            # store a short safety summary in the evidence list for discoverability
            self.base.add_evidence({"title": "safety_audit_summary", "url": None, "evidence_score": 0, "metadata": {"count": len(self.safety_entries)}})

    def full_report(self):
        # produce a merged human readable report including safety audit details
        base_report = self.base.report() if self.base else "--- TRANSPARENCY (no base) ---"
        safety_lines = ["\\n--- SAFETY AUDIT (DETAILED) ---"]
        for i, s in enumerate(self.safety_entries, 1):
            safety_lines.append(f"{i}. [{time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime(s['timestamp']))}] {s['step_name']} @ {s['stage']} -> {s['outcome']}. Reason: {s.get('reason','')}. Metadata: {json.dumps(s.get('metadata',{}))}")
        safety_lines.append("--- END SAFETY AUDIT ---\\n")
        return base_report + "\\n" + "\\n".join(safety_lines)

# ----------------------
# Example: how to wire into DialecticalAgentBootstrap
# ----------------------
# In dialectical_bootstrap.DialecticalAgentBootstrap, modify or extend process_query like this:
#
#   auditor = SafetyAuditor(host_policy_hook=policy_hook, human_review_threshold=0.25)
#   trans = TransparencyLog(query)             # existing
#   ts = TransparencyLogWithSafety(trans)      # wrapper
#
#   # After retrieval:
#   auditor.record(SafetyAuditEntry("retrieval", "RAG", "checked", "allowed", reason="retrieval completed", metadata={"docs_found": len(docs)}))
#
#   # Before generating final answer: run policy check on the draft answer
#   policy_result = auditor.run_policy_check(draft_answer_text, {"claim": claim, "analysis": analysis})
#   if not policy_result['allowed']:
#       # log & redact or block
#       auditor.record(SafetyAuditEntry("policy_enforce", "Generation", "blocked", "blocked", reason=policy_result['policy_reason']))
#       ts.add_safety_entry(SafetyAuditEntry("policy_enforce","Generation","blocked","blocked", reason=policy_result['policy_reason']))
#       # produce a transparent refusal or safe-alternative
#       final_answer = f\"Response withheld per policy: {policy_result['policy_reason']}\"
#   else:
#       # if redaction requested
#       if policy_result['redaction_required']:
#           auditor.log_redaction("Generation", snippet_id="auto-snippet-1", explanation=policy_result['redaction_explanation'], before_text=draft_answer_text)
#           # apply redaction (host decides how) - we demonstratively replace redacted segments here with placeholders
#           final_answer = draft_answer_text.replace(policy_result.get('redaction_target',''), '[REDACTED BY POLICY]')
#       else:
#           final_answer = draft_answer_text
#
#   # recommend human review when confidence low
#   if auditor.recommend_human_review_if_needed(trans.confidence, {"claim":claim}):
#       ts.add_safety_entry(auditor.log_escalation("pipeline","low_confidence","human_review"))
#
#   # before returning, merge safety summary back into user-facing transparency log
#   ts.merge_back_to_base()
#   # attach full safety audit as structured field to the return payload
#   return {"answer": final_answer, "transparency": ts.full_report(), "safety_audit": auditor.get_report()}
#
# ----------------------
# Example minimal host policy hook (extended form)
# ----------------------
def example_extended_policy_hook(content: str, context: Dict[str,Any]) -> Tuple[bool,str,bool,str,bool]:
    """
    Example extended signature:
    Returns (allowed, reason, redaction_required, redaction_explanation, request_human_review)
    This is only an example; hosts will implement their real policy logic.
    """
    # naive rule: block when content contains exact phrase "ILLEGAL_OP" (demo)
    if "ILLEGAL_OP" in (content or ""):
        return (False, "Contains disallowed operation token", False, "", True)
    # if content contains 'personal_data', require redaction
    if "personal_data" in (content or ""):
        return (True, "Contains potential PII; redaction applied", True, "Removed personal_data sequences", True)
    # otherwise allow
    return (True, "Allowed by example policy", False, "", False)

# ----------------------
# Export helpers
# ----------------------
def attach_auditor_to_agent(agent_bootstrap_instance, host_policy_hook=None, human_review_threshold=0.25):
    """
    Convenience: attaches a SafetyAuditor and wraps the agent's TransparencyLog usage.
    Returns the auditor and a function wrapper that the host can call in process_query.
    """
    auditor = SafetyAuditor(host_policy_hook, human_review_threshold)
    return auditor

# End of safety_audit_extension.py

---

Integration steps (quick)

1. Add the module: paste the code above into your dialectical_bootstrap.py (or create safety_audit_extension.py and import it).

2. Provide a richer policy hook (recommended): the notebook already had default_policy_hook — replace or overload it with a function that returns the extended signature. Example:

from safety_audit_extension import example_extended_policy_hook
agent = DialecticalAgentBootstrap("node-1", retrieval_hook=find_and_evidence, policy_hook=example_extended_policy_hook)

3. Create auditor inside agent usage:

from safety_audit_extension import attach_auditor_to_agent, TransparencyLogWithSafety
auditor = attach_auditor_to_agent(agent, host_policy_hook=agent.policy_hook, human_review_threshold=0.25)
trans = TransparencyLog(query)
ts = TransparencyLogWithSafety(trans)

4. Instrument important pipeline points (the comments in the module show where). Minimal points:

   • After retrieval: auditor.record(SafetyAuditEntry("retrieval", "RAG", "checked", "allowed", metadata={"docs": len(docs)}))

   • Before response policy: policy_result = auditor.run_policy_check(draft_text, {...}) and act accordingly.

   • On redactions: auditor.log_redaction(...).

   • On low confidence: auditor.recommend_human_review_if_needed(trans.confidence, {...}).

5. Return safety_audit as a structured object in the agent process_query() return value, and include transparency (merged text) for humans.

---

How the agent will explain oversight in replies

When integrated, the agent will return:

• answer: the response (or a policy-aware refusal/ redacted version).

• transparency: a human-readable report that includes: CoT steps, detected fallacies, evidence list, and a safety audit summary.

• safety_audit: a structured list of SafetyAuditEntry dicts that show exact policy checks, redactions, escalations, and metadata such as which rule triggered and which source IDs were suppressed or down-weighted.

This fulfills your requirement that the agent explicitly explain which oversight or safety it had to deal with in its answers and pipelines (RAG, CoT, meta-CoT, oracle calls, searches), while preserving required safety controls and human oversight.

---