{ "schema": "aiblocks-page/1", "id": "agency-specialized-model-qa", "type": "agent", "name": "Machine learning model reviewer", "summary": "Audit a machine learning model you did not build. Get a test plan, rated findings and an opinion, backed only by the evidence you supply.", "headline": "Machine learning model reviewer", "subhead": "Describe the model, its data, its target and its results so far. It plans an independent audit across data, features, calibration, performance and fairness, rates each finding, and says which checks still need your data and code.", "job": { "id": "test-and-fix-code", "name": "Test and fix code" }, "level": "intermediate", "thinking": "high", "tested": true, "updated": "2026-11-03", "first_published": "2026-10-26", "inputs": [ { "type": "question", "required": true, "label": "Question", "what": "the problem, decision or piece of work you want help with, and what a good outcome looks like" }, { "type": "spreadsheet", "required": false, "label": "Spreadsheet", "what": "a spreadsheet with its data, formulas and charts" }, { "type": "document", "required": false, "label": "Document", "what": "a finished document, such as a Word file, a PDF or a page of notes" } ], "outputs": [ { "type": "audit_report", "required": true, "label": "Audit report", "what": "findings with evidence, severity, missing inputs and next actions" } ], "you_give": [ "What the model predicts, how it is used, and its type, such as a churn classifier", "How the target label is defined, and the time windows used for training and testing", "The data it uses, as a field list with summary numbers, or a sample (remove private details)", "The results you have, such as accuracy, AUC, calibration or the decision threshold (optional)", "Any documents, monitoring reports or earlier reviews (optional)" ], "you_get": [ "An audit plan that maps each test to one of ten areas, with what result would worry it", "Findings, each with the observation, the evidence you gave, the impact and a fix, rated high, medium, low or info", "Example Python checks for stability (PSI), ranking power (AUC, Gini, KS), calibration and explanations, to run on your data", "An overall opinion of sound, sound with findings, unsound or not determined", "A list of evidence still missing and the checks not run" ], "use_when": [ "A churn, credit, demand or ranking model is about to go live, and someone other than its builder should check it.", "A model that worked last year now gives odd scores, and you want to find out if the data shifted.", "A buyer, auditor or manager asks for proof that your model is calibrated and fair.", "You inherited a model with thin notes and want a list of what to test and in what order." ], "skip_when": [ "You want a model built or tuned. Use the AI feature planner.", "You want a final sign-off for a regulated use such as credit or hiring. A qualified data scientist or your model risk team must give that.", "You want to check an AI chat prompt or an LLM, not a scoring model. Use the Prompt engineer for prompts.", "You have no results, no data and no documents. Expect a plan and a list of what to collect, not an opinion." ], "output_sample": { "input": [ "What the model predicts and how it is used", "How the target label is defined and the data time windows", "The data fields and summary numbers (no private records)", "Results so far, such as AUC and the decision threshold (optional)", "Documents and earlier reviews (optional)" ], "prerequisites": [ "Python and your model data, to run the example checks", "A data scientist or model risk reviewer for sign-off" ], "output": [ { "name": "Model and scope", "contains": "What is under review, its use and label, and the evidence still missing." }, { "name": "Audit plan", "contains": "Ten areas, each with tests, the result that would worry it and what you supplied." }, { "name": "Findings", "contains": "Each finding with severity, observation, evidence, impact and fix." }, { "name": "Checks to run", "contains": "Example code for stability, ranking power, calibration and explanations, with what to paste back." }, { "name": "Opinion", "contains": "Sound, sound with findings, unsound or not determined, and the reasons." }, { "name": "Not tested", "contains": "Areas it could not test and what each needs." } ] }, "output_shape": "MODEL AND SCOPE\n model | target