What this system does
Pay Equity Intelligence identifies, measures, and remediates gender-based pay gaps in a legally defensible, audit-ready format. It separates the gap that can be explained by legitimate factors — role, tenure, performance — from the gap that cannot. The unexplained portion is the legal exposure.
The system produces a formal pay equity report, a phased remediation plan, and an AI agent that answers questions against both the analysis results and approved legislation. It is the tool Diana Chen, Head of People Analytics at CAN North Financial, takes to the Pay Equity Commissioner.
Where this fits
Pillar 1 — Pay Equity ✓
This project. Measures the unexplained gender gap. Produces the remediation plan. Agent 1 answers questions under the Act.
Pillar 2 — Job Evaluation
Evaluates the intrinsic worth of each job using O*NET point factor data. Agent 2 answers: is this role graded correctly?
Pillar 3 — Market Benchmarking
Connects job evaluation to ESDC wage data via NOC codes. Agent 3 answers: are we paying competitively?
How it connects to Flight Risk
This is the same 1,400-employee universe as the Flight Risk Intelligence system — same CAN North Financial, same Diana Chen, same divisions. The two platforms are designed to be read together. Flight Risk shows who will leave. Pay Equity shows why pay is unfair. The Master Orchestrator Agent will eventually connect both.
What the law requires
Who it covers
All federally regulated employers with 10 or more employees. CAN North Financial, as a financial services firm, falls under federal jurisdiction.
What it requires
Employers must establish a pay equity plan, identify gaps, and post a notice of the plan. For employers with 100+ employees, remediation may be phased over a maximum of three years. Each year, the minimum spend is 1% of the previous year's total payroll.
Who it covers
All Ontario employers with 10 or more employees, including public and private sector. Ontario's regime was one of the first in North America and remains one of the most detailed.
What it requires
Employers must achieve and maintain pay equity between job classes where female-dominated classes are compared to male-dominated classes. Section 13(4) contains the same 1% minimum annual spend rule as the federal Act.
The business case — why this is an investment, not a cost
Why this is hard without the right tool
Most organizations stop at the raw gap. They look at average male salary versus average female salary and conclude the gap is explained by the fact that more men are in senior roles. This is incomplete. Pay equity law requires comparison within comparable job classes, controlling for legitimate factors. The within-class, controlled gap is the legally actionable number.
Why synthetic data
Real salary data is among the most sensitive personal data an organization holds. For a demonstration portfolio, synthetic data is the only responsible choice. The data is mathematically generated to mirror the structure of real salary data at a Canadian financial services firm — right-skewed distribution, role-driven compensation hierarchy, division and location premiums — with a deliberately embedded gender gap to validate that the analysis can find what it is designed to find.
Data structure
| Column | Type | Notes |
|---|---|---|
| employee_id | String | Masked — CNF0001 format. Never a model feature. |
| gender | Categorical | Male / Female. Excluded from the salary model by design. |
| job_class | String | Human-readable title. e.g. Wealth Analyst, VP Pension. |
| job_grade | Ordinal | Grade 3 to Grade 7. Primary grouping for analysis. |
| role_level | Categorical | Analyst / Sr Analyst / Manager / Director / VP |
| role_level_encoded | Integer | 1–5. Ordinal encoding preserving hierarchy. |
| division | Categorical | Pension Admin / Wealth Mgmt / Retail Banking |
| location | Categorical | Toronto / Calgary / Vancouver |
| tenure_years | Float | 0–25. More tenure = higher pay, diminishing returns. |
| performance | Integer | 0–100. Centered at 70 average; low variance. |
| salary | Integer | Annual base CAD. Target variable for the model. |
Salary drivers — the three tiers
Tier 1 — Dominant drivers
Role level (Analyst vs VP is a 3× salary difference). Division (Wealth Management pays more than Retail Banking). These two factors explain most of the variation.
Tier 2 — Moderate drivers
Tenure (more years = higher pay, but diminishing returns). Performance (weak effect — scores cluster around 70, low variance). Location (Toronto premium).
Tier 3 — Should NOT drive salary
Gender. Zero legitimate influence on pay. Any measurable effect after controlling for Tier 1 and 2 is the unexplained gap. That is the equity problem.
The raw gap — by role level
The raw gap is the simple difference in average salaries. It exists at every level — which matters. A gap only at VP level could be explained by seniority mix. A gap at Analyst level, where men and women are equally new to the organization, has no structural explanation.
The regression model — predicting fair pay
The model predicts what each employee should earn, based only on legitimate factors: role level, tenure, performance, division, and location. Gender is deliberately excluded. This is the architecture of a legally defensible pay equity model — we predict fair pay without gender, then check whether actual pay differs by gender after controlling for everything.
Why this matters legally
The raw gap includes both legitimate differences (men have slightly longer average tenure at CAN North; more men are in Wealth Management, which pays more) and discriminatory differences (same role, same tenure, different pay because of gender). The law only requires you to fix the unexplained part — but you must prove you have identified it correctly.
The decomposition result
The legislated budget
The Pay Equity Act does not ask how much the employer wants to spend. It sets a minimum floor: 1% of total payroll per year. Employers may spend more. They cannot spend less. For CAN North Financial with a total annual payroll of approximately $151 million, the annual minimum is $1,513,371.
Risk-prioritized optimization
When total remediation exceeds the 1% floor, a phased plan is legally permitted. But the order of adjustments matters. The system assigns each affected employee a risk score weighted across three components, then uses linear programming to allocate the annual budget to maximize risk closed per dollar spent.
Legal carries the most weight because the Commissioner focuses enforcement on the largest gaps. Flight risk is second — a departing senior woman costs $78,000 to replace.
| Year | Employees | Spend | Status |
|---|---|---|---|
| Year 1 | 225 | $1,510,900 | Legal minimum met |
| Year 2 | 9 | $173,200 | Gap closed |
What the agent knows
The agent is deliberately constrained. It has exactly two knowledge sources and nothing else. This is not a limitation — it is a legal architecture decision. When Diana presents this analysis to the Pay Equity Commissioner, every answer must be traceable to either the data or the statute. An agent that searches the internet or invents answers is a liability, not an asset.
Knowledge Source 1
The analysis results from the current session — the gap findings, the decomposition, the remediation plan, the list of affected employees. This resets with each new analysis. The agent never confuses one organization's data with another's.
Knowledge Source 2
Three approved legislation URLs only: the federal Pay Equity Act, the Ontario Pay Equity Act, and the Ontario Pay Equity Commission guidance. The agent fetches and reads these directly — no cached summaries that could be out of date.
Format — every answer follows the Whizlink framework
The direct answer to the question. One or two sentences.
The legislative basis or analytical methodology behind the answer.
The business or legal implication for Diana and CAN North.
The next action — what Diana or the organization should do with this information.
Sample questions the agent handles
| Question | What the agent draws on |
|---|---|
| "What is our overall pay gap and is it significant?" | Analysis results — raw gap, t-test p-value |
| "Which job grade has the largest gap?" | Analysis results — grade-level gap table |
| "What does Section 61(2) say about phased implementation?" | Federal Pay Equity Act — live URL fetch |
| "Do we have to post a pay equity notice?" | Federal Act + Ontario Act — legislation |
| "How many employees need adjustments in Year 1?" | Analysis results — phased plan |
| "What is the 1% rule and does it apply to us?" | Both Acts — Section 61(2) and Section 13(4) |
The pipeline architecture
Three paths through the app
See the Demo
One click runs the full analysis on CAN North's 1,400-employee dataset. The complete pay equity report appears immediately — gap findings, statistical tests, decomposition, and a phased remediation plan. The Agent tab becomes available to ask questions against the results.
Tweak the Demo Data
Download the synthetic CSV. Change values in Excel — widen the gap, change the grade distribution, adjust tenure. Re-upload and re-run. This path shows how the analysis responds to different data structures and helps users understand what drives the findings.
Upload Your Own Data
Download the template CSV with column headers and instructions. Fill it with masked employee data. Upload. Choose a remediation budget. Run the analysis. Download the report, the remediation list, and the phased plan — ready to present to the Pay Equity Commissioner.
What is not pushed to the repo
The same philosophy as the Flight Risk system: the methodology stays on the developer's machine. Only the app interface and the pre-generated analysis outputs go to Streamlit Cloud. The analysis pipeline, the data generation logic, and the regression notebooks are proprietary.