---
title: APERTURE AUDIT
emoji: 🔍
colorFrom: purple
colorTo: gray
sdk: streamlit
sdk_version: "1.38.0"
python_version: "3.10"
app_file: app.py
pinned: false
---

# Aperture Audit — Model-Agnostic Explainability Dashboard

**Portfolio project 4 of 5** — a demo response to the regulatory pressure
tech giants like Meta face over unexplainable ad-targeting and
recommendation algorithms. Frameworks like the EU AI Act impose real
penalties for high-stakes automated decisions (credit, ads, hiring) that
can't be explained at the individual level. This project audits a credit
model's decisions with two independently-derived explanation methods that
are checked against each other, not just asserted.

> ⚠️ **All data in this project is synthetic.** `data/credit_applications.csv`
> is generated by `generate_synthetic_data.py` from a known synthetic
> weight vector (see the file for the exact formula). No real applicant,
> credit bureau, or financial data is used anywhere in this repo.

## Why this exists

Explainability tools are often trusted uncritically: a SHAP value looks
authoritative, so it's treated as ground truth. But SHAP implementations
can have bugs, and approximate methods (KernelSHAP, LIME) are themselves
estimates that can be wrong. This project builds in a validation step:
because logistic regression's SHAP values have an exact closed form, this
demo can generate a second, independently-derived explanation (LIME, built
from scratch via local perturbation) and check that they agree — a
legitimate audit technique for any explainability pipeline, not just this
one.

## Architecture

```
credit application (synthetic)
    |
    v
Model                      <- model.py
    logistic regression, chosen deliberately: it's the case where
    SHAP attribution has an exact closed form, not an approximation
    |
    v
    +-------------------------+-------------------------------+
    |                                                          |
    v                                                          v
Exact SHAP attribution        LIME local surrogate explanation
<- explain/shap_exact.py      <- explain/lime_local.py
   phi_i = coef_i * (x_i - mean_i)   perturb applicant, refit locally,
   (closed form, no sampling)        extract local linear weights
    |                                                          |
    +-------------------------+-------------------------------+
                               |
                               v
                    Cross-check panel: do the two
                    independently-derived explanations
                    agree on direction and rough magnitude?
```

## Try it

```bash
pip install -r requirements.txt
streamlit run app.py
```

Pick a synthetic applicant from the dataset (or set feature values manually
with the sliders) and step through the three tabs: the exact SHAP-style
attribution chart, the LIME cross-check (with a per-feature agreement
table), and a plain-language audit narrative citing the top 3 drivers of
the decision — the level of detail EU AI Act Article 13-style regulations
require for high-stakes automated decisions.

## Project structure

```
xai-compliance-dashboard/
├── app.py                        # Streamlit UI
├── model.py                      # logistic regression training + inference
├── generate_synthetic_data.py    # produces data/credit_applications.csv
├── explain/
│   ├── shap_exact.py             # exact closed-form SHAP attribution
│   └── lime_local.py             # from-scratch LIME local surrogate
├── data/
│   └── credit_applications.csv   # SYNTHETIC credit application data
└── requirements.txt
```

## Production upgrade path

| Demo component | Production equivalent |
|---|---|
| Logistic regression | Any production model (XGBoost, neural net) |
| `shap_exact.py` closed-form attribution | Real `shap` package: `TreeExplainer` (tree models) or `KernelExplainer` (any model) |
| `lime_local.py` from-scratch LIME | The `lime` package, or a hardened re-implementation with categorical feature support |
| Streamlit dashboard | FastAPI + React audit UI, with per-decision explanation logging for regulatory record-keeping |
| Single logistic model | Full audit trail across model versions, with drift-triggered re-explanation |

## Project landing page

`docs/index.html` is a standalone, single-file static landing page (no build step) summarizing the project's results, method, and findings. To host it live on GitHub Pages: repo **Settings → Pages → Source: Deploy from a branch → Branch: main, folder: /docs → Save**. It'll be live within a minute or two at `https://data-geek-astronomy.github.io/APERTURE_AUDIT/`.