OpenChainGraph Suite · ART-763 · Port of Apex #104 · AL-111

AI Displacement Risk Screener

Automation-exposure score (0–100) from SOC task-composition data, with a risk tier and adjacent lower-exposure pivots. Select a job (or leave it custom), set the task sliders, run.

probabilities: Frey-Osborne 2013 vintage (stale)task splits: Apex approximations labelled O*NET 2024salaries: labelled BLS OES 2025 (UNGROUNDED)Zero PIIClient-side only

🔒 All inputs are processed locally in your browser. No data is transmitted. Do not enter real personal data; use synthetic or anonymised inputs only.

Scope & reliance

The score is a fixed-weight composite: 0.40 × automation probability + 0.40 × routine-task fraction (routine cognitive + routine manual over 100) + 0.20 × the selected sector exposure factor, clamped to 0..100 and rounded. A SOC code selects an occupation from the embedded 62-row table; a blank selection estimates the probability from the sliders (slope 0.85, floor 0.05, cap 0.99). The embedded probabilities are Frey-Osborne 2013-vintage estimates, the task splits are Apex-authored approximations, and the median-salary cells labelled BLS OES 2025 stayed ungrounded at porting time (BLS HTTP 403); salary figures are informational only and stay out of the score, with every vintage and grounding flag carried in the kernel metadata.

Job & profile

Job title or SOC code (blank = custom estimate)

ed_level (education, informational)

years_role (recorded, not in the arithmetic)

sector_exp (sector exposure factor)

Task composition (sliders; a SOC pick pre-fills)

rc, routine cognitive 15%

rm, routine manual 4%

nra, non-routine analytical 60%

nri, non-routine interpersonal 16%

nrm, non-routine manual 5%

Result

n/a

Risk score / 100

n/a

Tier

n/a

Education-adjusted (info)

n/a

Factor	Value

Adjacent lower-exposure pivots

Execution hash & kernel output

execution_hash: n/a

{}

Proof status

This node ships compute_proof_ready: deferred. No formal correctness proof has been produced for this kernel; the result above is a deterministic recompute, verified by a property-test floor, not a proven one.

Ask your agent

Copy this paragraph into Claude, OpenClaw, or any MCP-aware agent to run this exact tool, with this sample, and verify the artifact.

Run the AINumbers MCP tool `ai_displacement_risk_screener`. Task: AI displacement risk screener ported from ApexLogics Apex #104 (AL-111): a 0..100 composite score (0.40 automation probability + 0.40 routine-task fraction + 0.20 sector exposure) with a Low/Medium/High/Critical tier, an education-adjusted informational score, and up to three adjacent lower-exposure pivots gated at similarity >= 60.
Call it with arguments: {"policy_parameters":{"soc_code":"15-1132","rc":15,"rm":4,"nra":60,"nri":16,"nrm":5,"sector_exp":0.45,"ed_level":"bachelor","years_role":5}}
Verify before trusting: call `verify_execution_hash` on mcp.ainumbers.co (https://mcp.ainumbers.co/mcp) with the parameter `claimed_hash` set to the returned `execution_hash`, passing the full artifact the run returned (the object containing `policy_parameters` + `output_payload` + `execution_hash`; equivalently `policy_parameters` + `output_payload` with `claimed_hash`), not the bare hash string.
Return the ledger link https://ledger.ainumbers.co/ so a human can re-verify without contacting us.
PII rule: All inputs are processed locally in your browser. No data is transmitted. Do not enter real personal data — use synthetic or anonymised inputs only.
Open the tool with the sample prefilled: https://ainumbers.co/chaingraph/art-763-ai-displacement-risk-screener.html#p=v1.H4sIAAAAAAAA_wF2AIn_eyJzb2NfY29kZSI6IjE1LTExMzIiLCJyYyI6MTUsInJtIjo0LCJucmEiOjYwLCJucmkiOjE2LCJucm0iOjUsInNlY3Rvcl9leHAiOjAuNDUsImVkX2xldmVsIjoiYmFjaGVsb3IiLCJ5ZWFyc19yb2xlIjo1femBNi12AAAA