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| <nav> | |
| <div class="wrap"> | |
| <div class="brand"><span class="dot"></span> APA · Aperture Audit Lab</div> | |
| <div class="navlinks"> | |
| <a href="#results">Results</a> | |
| <a href="#method">Method</a> | |
| <a href="#findings">Findings</a> | |
| <a href="https://github.com/data-geek-astronomy/APERTURE_AUDIT">GitHub</a> | |
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| <section class="hero"> | |
| <div class="wrap"> | |
| <span class="badge">RESEARCH PREVIEW · APA</span> | |
| <h1>Aperture Audit</h1> | |
| <p class="sub">A credit-decision dashboard where every prediction ships with an exact, closed-form SHAP-style attribution — cross-checked against an independently derived LIME explanation built from scratch, not just asserted.</p> | |
| <div class="cta-row"> | |
| <a class="btn btn-primary" href="https://huggingface.co/spaces/Darkweb007/APERTURE_AUDIT">Launch live demo</a> | |
| <a class="btn btn-secondary" href="https://github.com/data-geek-astronomy/APERTURE_AUDIT">Read the code</a> | |
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| </section> | |
| <section id="results"> | |
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| <div class="eyebrow">HEADLINE RESULT</div> | |
| <div class="headline-card">Two <b>independently-derived explanation methods</b> — exact SHAP attribution and a from-scratch LIME local surrogate — agreed on the direction of every feature's effect in <b>100% of a 140-decision cross-check</b> (20 applicants × 7 features).</div> | |
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| <div class="metric-card"><div class="metric-value">100%</div><div class="metric-label">SHAP / LIME sign agreement across 140 feature-decisions checked</div></div> | |
| <div class="metric-card"><div class="metric-value">7</div><div class="metric-label">credit features attributed per decision, ranked by effect size</div></div> | |
| <div class="metric-card"><div class="metric-value">0.99</div><div class="metric-label">average LIME local surrogate fidelity (R²)</div></div> | |
| <div class="metric-card"><div class="metric-value">2</div><div class="metric-label">independently-derived explanation methods per decision</div></div> | |
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| </section> | |
| <section id="method"> | |
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| <div class="eyebrow">METHOD</div> | |
| <h2 style="margin:0 0 6px; font-size:1.6rem;">Two explanations, cross-checked, not just one asserted</h2> | |
| <p style="color:var(--muted); max-width:640px; margin:0 0 10px;">Logistic regression is chosen deliberately: it's the case where SHAP attribution has an exact closed form, making it possible to verify the second, sampling-based method against it.</p> | |
| <div class="steps"> | |
| <div class="step"><div class="step-num"></div><div><div class="step-title">Train</div><div class="step-desc">A logistic regression credit model is trained on synthetic applications with a known ground-truth generating function.</div></div></div> | |
| <div class="step"><div class="step-num"></div><div><div class="step-title">Attribute (SHAP)</div><div class="step-desc">Each feature's exact contribution to a decision is computed in closed form: coefficient × (value − population mean).</div></div></div> | |
| <div class="step"><div class="step-num"></div><div><div class="step-title">Attribute (LIME)</div><div class="step-desc">Independently, 400 small random perturbations around the applicant are run back through the real model, and a local weighted regression recovers a second explanation with zero knowledge of the model's internals.</div></div></div> | |
| <div class="step"><div class="step-num"></div><div><div class="step-title">Cross-check</div><div class="step-desc">The two explanations are compared feature by feature, on direction and magnitude, using the same additive formula for both.</div></div></div> | |
| <div class="step"><div class="step-num"></div><div><div class="step-title">Narrate</div><div class="step-desc">The top 3 drivers of each decision are surfaced in plain language — the level of detail EU AI Act-style regulations require for high-stakes automated decisions.</div></div></div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="findings"> | |
| <div class="wrap"> | |
| <div class="eyebrow">FINDINGS</div> | |
| <h2 style="margin:0 0 20px; font-size:1.6rem;">What the synthetic test runs showed</h2> | |
| <div class="findings"> | |
| <div class="finding">An initial version of the cross-check compared raw LIME coefficient signs against SHAP's deviation-adjusted contributions — an apples-to-oranges mismatch that produced only 2/7 agreement. Fixing the comparison to use the same formula for both raised agreement to 100%.</div> | |
| <div class="finding">Recovered model coefficients matched the sign of the synthetic data's true generating weights on all 7 features (e.g. late payments and debt-to-income both correctly learned as negative effects on approval).</div> | |
| <div class="finding">LIME's local surrogate fidelity stayed above 0.99 R² across sampled applicants, meaning the local linear approximation captures the real model's behavior almost exactly in each applicant's neighborhood.</div> | |
| </div> | |
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| </section> | |
| <footer> | |
| <div class="wrap"> | |
| <div>All data on this page is synthetic — part of a 5-project AI engineering portfolio.</div> | |
| <div class="footer-links"> | |
| <a href="https://huggingface.co/spaces/Darkweb007/APERTURE_AUDIT">Live demo</a> | |
| <a href="https://github.com/data-geek-astronomy/APERTURE_AUDIT">GitHub repo</a> | |
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