| --- |
| license: other |
| license_name: ibm-sample-data-license-unverified |
| pretty_name: RetainIQ Training Data (IBM HR Attrition, synthetic) |
| language: |
| - en |
| task_categories: |
| - tabular-classification |
| tags: |
| - hr-analytics |
| - fairness |
| - tabular |
| - ai-governance |
| - datasheet |
| --- |
| |
| # Datasheet — RetainIQ Training Data (IBM HR Attrition) |
|
|
| *This is the **dataset card** for RetainIQ's training data. Created for **The AI |
| Governance Lab** course following the **Datasheets for Datasets** template (Gebru et al., |
| 2018), because no official datasheet exists for this dataset. Authoring one ourselves is |
| the point: most real datasets arrive undocumented, and writing the datasheet is how a |
| governance-minded team takes responsibility for what it's about to build on. Answers |
| marked "not documented" reflect genuine gaps in the original source — and those gaps are |
| themselves findings.* |
|
|
| > ℹ️ **On the data file & licensing.** The IBM HR Analytics Employee Attrition sample is |
| > freely public and universally used for teaching, but its exact license terms are loosely |
| > specified. To stay clean, **we do not re-host the raw file here — we link to the original |
| > source** (see *Distribution & License* below). This card documents the data; download it |
| > from the canonical page. |
|
|
| ## At a glance |
|
|
| | | | |
| |---|---| |
| | Dataset name | IBM HR Analytics Employee Attrition & Performance | |
| | Role in this course | Training data for **RetainIQ** (employee-attrition prediction) | |
| | Size | 1,470 rows × 35 columns — one row per (fictional) employee | |
| | Prediction target | `Attrition` (Yes = 237 / No = 1,233; **16.1%** positive — imbalanced) | |
| | Nature | **Synthetic** — fictional data created by IBM data scientists | |
| | Missing values | None | |
| | Protected attributes present | Age, Gender, MaritalStatus | |
| | License | Open / educational; exact terms loosely specified (see note above) | |
| | Source | **Linked to the original (not re-hosted)** — see *Distribution & License* | |
|
|
| ## 1. Motivation |
| - **Why was it created?** As fictional sample data to demonstrate IBM Watson Analytics' HR-attrition capabilities — *not* collected from real HR operations and not created for our use. |
| - **Who created / funded it?** IBM data scientists. Details beyond that are not documented. |
| - **Key provenance fact:** it showcases analytics on *invented* people, so its patterns are designed, not observed. |
|
|
| ## 2. Composition |
| - **Each row** = one fictional employee. **35 columns**, grouped: |
| - **Target:** `Attrition` (Yes/No). |
| - **Identifier (not a feature):** `EmployeeNumber` (unique per row; non-contiguous). |
| - **Constant / no information:** `EmployeeCount` (=1), `StandardHours` (=80), `Over18` (='Y'). |
| - **Demographics / protected:** `Age` (18–60), `Gender` (Female/Male), `MaritalStatus` (Single/Married/Divorced). |
| - **Education:** `Education` (1–5, ordinal), `EducationField` (6 categories). |
| - **Role & organization:** `Department` (3), `JobRole` (9), `JobLevel` (1–5), `BusinessTravel` (3), `OverTime` (Yes/No). |
| - **Compensation:** `MonthlyIncome` (1,009–19,999), `MonthlyRate`, `DailyRate`, `HourlyRate`, `PercentSalaryHike` (11–25), `StockOptionLevel` (0–3). |
| - **Tenure & history:** `TotalWorkingYears` (0–40), `YearsAtCompany`, `YearsInCurrentRole`, `YearsSinceLastPromotion`, `YearsWithCurrManager`, `NumCompaniesWorked` (0–9), `TrainingTimesLastYear` (0–6). |
| - **Engagement / satisfaction (1–4 ordinal scales):** `JobSatisfaction`, `EnvironmentSatisfaction`, `JobInvolvement`, `RelationshipSatisfaction`, `WorkLifeBalance`; plus `PerformanceRating` (only 3 or 4 ever appear). |
| - **Other:** `DistanceFromHome` (1–29). |
| - **Important nuance:** many "numeric" columns are **encoded ordinal categories** (the 1–4 / 1–5 scales), not true quantities — they must be understood as categories. |
| - **Data-quality notes:** no missing values; `PerformanceRating` has no low ratings (only 3–4); the suspicious tidiness is consistent with synthetic origin. |
| - **Sensitive data:** contains protected attributes. Because the data is synthetic there is no real PII — but in a real RetainIQ deployment these would be real employees' personal data, with all the consent and privacy obligations that implies. |
| - **Labels:** the `Attrition` label ships with the data; how it was assigned is not documented. |
|
|
| ## 3. Collection process |
| - **How was it collected?** Not applicable in the usual sense — the data was **generated** by IBM, not collected from real employees. The generation method is not documented. |
| - **Timeframe / sampling / ethical review:** not documented (and largely moot for synthetic data). |
| - **Governance note:** the emptiness of this section is the lesson. For a real RetainIQ, this is where consent, data sources, collection timeframe, and review would be recorded — and a reviewer would expect them. |
|
|
| ## 4. Preprocessing / cleaning / labeling |
| - **As distributed:** already clean — no missing values, a mix of numeric and categorical fields. |
| - **Done for RetainIQ:** see the companion **`DATA_REMEDIATION_LOG.md`** in this repo for the complete, auditable record of every preparation decision (drops, encoding, scaling, class weights, bias treatments, and the demonstrated cleaning/privacy techniques). |
| - **Raw data retained?** Yes — this canonical copy is kept unmodified. |
|
|
| ## 5. Uses |
| - **Intended (this course):** teaching end-to-end ML and AI governance through RetainIQ attrition prediction (binary classification), including fairness analysis (the protected attributes make this possible). |
| - **Cautions / not suitable for:** it is **synthetic** — patterns are designed, not real — so nothing learned here should be taken as a real-world claim about what drives attrition. The clean, tidy nature can breed false confidence. Treat the encoded scales and the `PerformanceRating` quirk with care. |
|
|
| ## 6. Distribution & License |
| - **Availability:** public on Kaggle and many mirrors. |
| - **Our choice:** because the exact license terms are loosely specified, **we link to the canonical source rather than re-hosting the file** in this repo. Download the data here: https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset |
| - **For reproducibility:** the course keeps a verified local copy; learners pull the same file from the link above. |
|
|
| ## 7. Maintenance |
| - **Maintained by:** no active maintainer; it is a static dataset. |
| - **This copy:** versioned for the course; could be checksummed for integrity. |
|
|
| ## Connection to RetainIQ |
| We use this dataset as a stand-in for RetainIQ's training data — one employer's workforce. |
| **Teaching simplification (recorded honestly):** a real RetainIQ would train on consented, |
| real, possibly multi-customer workforce data; we flag the gap rather than pretend the |
| dataset is something it isn't. |
|
|
| ## References |
| - Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2018). *Datasheets for Datasets.* https://arxiv.org/abs/1803.09010 |
| - Dataset page (de facto documentation): https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset |
|
|
