Buildability / Receipt
This public receipt window renders only fields present in the canonical receipt object, deterministic fixture receipt, or canonical evidence receipt. Missing compute, demo, hash, signature, approval, telemetry, and adoption fields stay explicit.
Public buildability page receipt window
/buildability/an-empirical-evaluation-of-the-risks-of-ai-model-updates-using-clinical-data-stability-arbitrariness-and-fairness
Subject: An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Data
{"file name": "input.pdf", "number of pages": 8, "author": "Ioannis Bilionis; Ricardo C. Berrios; Luis Fernandez-Luque; Carlos Castillo"
Truth Boundary
Buildability surfaces only report computed viability and proof receipts. They do not claim live production usage, pilot outcomes, founder sign-off, public Brier calibration, judge divergence, or external adoption unless explicitly sourced.
Compute
{"file name": "input.pdf", "number of pages": 8, "author": "Ioannis Bilionis; Ricardo C. Berrios; Luis Fernandez-Luque; Carlos Castillo", "title": "An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness", "creation date": null, "modification date": null
Inference
{"file name": "input.pdf", "number of pages": 8, "author": "Ioannis Bilionis; Ricardo C. Berrios; Luis Fernandez-Luque; Carlos Castillo", "title": "An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness", "creation date": null, "modification date": null
Hardware
{"file name": "input.pdf", "number of pages": 8, "author": "Ioannis Bilionis; Ricardo C. Berrios; Luis Fernandez-Luque; Carlos Castillo", "title": "An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness", "creation date": null, "modification date": null
Receipt path
/buildability/an-empirical-evaluation-of-the-risks-of-ai-model-updates-using-clinical-data-stability-arbitrariness-and-fairness
Paper ref
an-empirical-evaluation-of-the-risks-of-ai-model-updates-using-clinical-data-stability-arbitrariness-and-fairness
arXiv id
2604.23954
Generated at
2026-04-28T15:19:32.788Z
Evidence freshness
stale
Last verification
2026-04-28T15:19:32.788Z
Sources
3
References
0
Coverage
50%
Lineage hash
f7580dfb0e5dd013da507fcfe6886849d1b7306cff31dd6876d31f9b3a0ba01e
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Some score or evidence fields are outside the preferred freshness window.
repo_url
references