diff --git a/assets/explainers-ui.js b/assets/explainers-ui.js
index b42c02d..27316ef 100644
--- a/assets/explainers-ui.js
+++ b/assets/explainers-ui.js
@@ -14,11 +14,11 @@
   const projectAnchors = {
     'COMPAS': 'project-compas',
     'AI Fair Recruitment': 'project-hiring',
-    'Ai Fair Recrutment Dataset': 'project-hiring',
     'German Credit Lending': 'project-credit',
     'Insurance Denial': 'project-insurance',
     'Benefits Denial': 'project-benefits',
     'Healthcare Readmission': 'project-readmission',
+    'Tenant Screening': 'project-tenant',
   };
 
   function escapeHtml(value) {
diff --git a/explainers/equal-opportunity.html b/explainers/equal-opportunity.html
index ab0d807..99f6885 100644
--- a/explainers/equal-opportunity.html
+++ b/explainers/equal-opportunity.html
@@ -270,7 +270,7 @@ <h3 id="4-small-subgroups-make-both-gaps-noisy">4. Small subgroups make both gap
 <h2 id="related-concepts">Related Concepts</h2>
 <ul><li><a href="equalized-odds.html">What Is Equalized Odds?</a> - the stricter metric Equal Opportunity relaxes, and the one that catches what Equal Opportunity alone cannot.</li><li><a href="false-positives-vs-false-negatives.html">False Positives vs. False Negatives in Medical Risk Models</a> - the per-threshold trade-off between the two error types Equal Opportunity and Equalized Odds split apart.</li><li><a href="demographic-parity.html">What Is Demographic Parity?</a> - a fairness definition that ignores ground truth entirely, unlike Equal Opportunity and Equalized Odds which both condition on it.</li><li><a href="label-bias.html">What Is Label Bias?</a> - why the &quot;true&quot; in true positive rate is only as trustworthy as the label it&#x27;s measured against.</li></ul>
 <h2 id="related-projects-in-this-repo">Related Projects in This Repo</h2>
-<ul><li><a href="../index.html#project-compas"><code>COMPAS/</code></a> - the audit where Equal Opportunity and Equalized Odds coincide, because the true-positive-rate gap dominates.</li><li><a href="../Tenant%20Screening/"><code>Tenant Screening/</code></a> - the audit where they diverge, because the false-positive-rate gap is actually the larger one.</li></ul>
+<ul><li><a href="../index.html#project-compas"><code>COMPAS/</code></a> - the audit where Equal Opportunity and Equalized Odds coincide, because the true-positive-rate gap dominates.</li><li><a href="../index.html#project-tenant"><code>Tenant Screening/</code></a> - the audit where they diverge, because the false-positive-rate gap is actually the larger one.</li></ul>
 <h2 id="further-reading">Further Reading</h2>
 <ul><li><a href="https://arxiv.org/abs/1610.02413" target="_blank" rel="noreferrer noopener">Hardt, M., Price, E., Srebro, N. (2016): Equality of Opportunity in Supervised Learning</a> - the paper that introduced Equal Opportunity as a relaxation of Equalized Odds.</li><li><a href="https://arxiv.org/abs/1703.00056" target="_blank" rel="noreferrer noopener">Chouldechova, A. (2017): Fair Prediction with Disparate Impact</a> - the impossibility result underlying why relaxations like Equal Opportunity exist in the first place.</li><li><a href="https://fairmlbook.org/classification.html" target="_blank" rel="noreferrer noopener">Barocas, S., Hardt, M., Narayanan, A. (2019): <em>Fairness and Machine Learning</em></a> - situates Equal Opportunity among the full family of classification-based fairness criteria.</li></ul>
 <p><em>Part of <a href="https://instagram.com/thefaircodeproject" target="_blank" rel="noreferrer noopener">The Fair Code Project</a> - exposing and fixing algorithmic bias with real data and open code.</em></p></article>
diff --git a/explainers/fairness-through-unawareness.html b/explainers/fairness-through-unawareness.html
index c7e15e1..9c43bc3 100644
--- a/explainers/fairness-through-unawareness.html
+++ b/explainers/fairness-through-unawareness.html
@@ -267,7 +267,7 @@ <h3 id="4-it-provides-no-way-to-measure-whether-it-worked-without-checking-a-rea
 <h2 id="related-concepts">Related Concepts</h2>
 <ul><li><a href="proxy-variables.html">Proxy Variables</a> - the mechanism (correlated features standing in for the removed attribute) that makes fairness through unawareness fail.</li><li><a href="mitigation-strategies.html">What Are Pre-, In-, and Post-Processing Fairness Mitigations?</a> - where &quot;unawareness&quot; sits as the first, weakest step in a five-strategy ladder, and what the stronger steps do differently.</li><li><a href="demographic-parity.html">What Is Demographic Parity?</a> - the metric used to measure the gap in the Tenant Screening example above.</li></ul>
 <h2 id="related-projects-in-this-repo">Related Projects in This Repo</h2>
-<ul><li><a href="../Tenant%20Screening/"><code>Tenant Screening/</code></a> - the audit above, where dropping race increased the measured gap rather than closing it.</li><li><a href="../index.html#project-insurance"><code>Insurance Denial/</code></a> - a second real audit in this repo&#x27;s benchmark harness where the same <code>unawareness</code> strategy is applied and measured independently.</li></ul>
+<ul><li><a href="../index.html#project-tenant"><code>Tenant Screening/</code></a> - the audit above, where dropping race increased the measured gap rather than closing it.</li><li><a href="../index.html#project-insurance"><code>Insurance Denial/</code></a> - a second real audit in this repo&#x27;s benchmark harness where the same <code>unawareness</code> strategy is applied and measured independently.</li></ul>
 <h2 id="further-reading">Further Reading</h2>
 <ul><li><a href="https://doi.org/10.1145/1401890.1401959" target="_blank" rel="noreferrer noopener">Pedreschi, D., Ruggieri, S., Turini, F. (2008): Discrimination-Aware Data Mining</a> - an early formal treatment of why removing a sensitive attribute doesn&#x27;t remove its statistical effect.</li><li><a href="https://arxiv.org/abs/1703.06856" target="_blank" rel="noreferrer noopener">Kusner, M. et al. (2017): Counterfactual Fairness</a> - proposes a causal alternative that explicitly accounts for what unawareness misses; see also <a href="counterfactual-fairness.html">Counterfactual Fairness</a>.</li><li><a href="https://fairmlbook.org/introduction.html" target="_blank" rel="noreferrer noopener">Barocas, S., Hardt, M., Narayanan, A. (2019): <em>Fairness and Machine Learning</em></a> - Chapter 1 covers &quot;fairness through unawareness&quot; directly as one of the field&#x27;s earliest and most persistently re-discovered mistakes.</li></ul>
 <p><em>Part of <a href="https://instagram.com/thefaircodeproject" target="_blank" rel="noreferrer noopener">The Fair Code Project</a> - exposing and fixing algorithmic bias with real data and open code.</em></p></article>
diff --git a/scripts/build_explainers.py b/scripts/build_explainers.py
index c41defe..c84bca3 100644
--- a/scripts/build_explainers.py
+++ b/scripts/build_explainers.py
@@ -42,6 +42,7 @@
     "Insurance Denial": "project-insurance",
     "Benefits Denial": "project-benefits",
     "Healthcare Readmission": "project-readmission",
+    "Tenant Screening": "project-tenant",
 }