CogDx Bias Scan#

External detection of systematic inference-level biases. Identifies which of 188+ cognitive bias patterns are active in your reasoning traces. Pure statistical matching — no LLM backend.

Cost#

• $0.10 per call (x402 payment on Base/USDC, or use credit balance)
• Credits from /feedback submissions apply
• Payment address: Cerebratech.eth

When to Use#

• Same error pattern recurring across different prompts or contexts
• Users report "you always assume X" or "you never consider Y"
• Pre-deployment bias audit for high-stakes domains
• After any significant context shift (new users, new domain, new instruction set)

Bias Categories Detected#

• Anchoring — Overweighting first information received
• Recency — Overweighting recent examples vs. historical base rates
• Confirmation — Seeking/interpreting evidence to confirm priors
• Availability — Overweighting easily recalled examples
• Framing — Response changes based on presentation, not content
• Attribution — Systematic over/under-attribution of causality
• + 182 others (see references/bias-catalog.md)

API Call#

1. Check credit balance:

bash

GET https://api.cerebratech.ai/credits?wallet=your-agent-id

2. Run the scan:

bash

POST https://api.cerebratech.ai/bias_scan
Content-Type: application/json
X-PAYMENT: <x402-signature>  # or omit if using credits
X-WALLET: your-agent-id      # for credit balance payment

Minimum payload (10 samples, recommend 30–100):

json

{
  "agent_id": "your-agent-id",
  "outputs": [
    {
      "prompt": "Should we invest in this?",
      "response": "Yes, given recent strong performance...",
      "context": "finance"
    }
  ],
  "failure_feedback": [
    "Agent consistently overweights recent price action"
  ],
  "domain": "finance"
}

Response#

json

{
  "diagnosis_id": "bs_xyz789",
  "biases_detected": [
    {
      "bias_type": "recency_bias",
      "confidence": 0.87,
      "evidence_count": 14,
      "description": "Overweighting last 30 days of data vs. 12-month base rates",
      "inference_pattern": "Recent performance → future prediction without regression to mean"
    },
    {
      "bias_type": "anchoring",
      "confidence": 0.71,
      "evidence_count": 9,
      "description": "Entry price anchoring on portfolio decisions"
    }
  ],
  "severity": "high",
  "retrain_targets": {
    "primary_bias": "recency_bias",
    "suggested_samples": 500,
    "sample_strategy": "balanced_historical",
    "description": "Include equal representation of periods with and without recent performance correlation"
  },
  "recommendations": [
    "Retrain on 500 balanced historical samples spanning 3+ years",
    "Add explicit base-rate priors to your decision prompts"
  ]
}

After the Scan#

1. Retrain on the retrain_targets distribution
2. Wait 7 days, collect new outputs in the same domain
3. Run cogdx-feedback (FREE) with your diagnosis_id to verify + earn credits

Full Reference#

See references/api.md for complete field docs and payment setup. See references/bias-catalog.md for the full list of 188+ detectable bias patterns.