Add distributionally robust optimization (DRO) for fairness explainer

CONTRIBUTORS.md

@@ -55,7 +55,7 @@ the [Git identity map](#git-identity-map) so `git shortlog -sne` can be reconcil
 
 | | Who | Role |
 |:--|-----|------|
-| <a href="https://github.com/yakew7"><img src="https://github.com/yakew7.png" width="48" height="48" alt="yakew7"></a> | **Yash Kewlani** - [@yakew7](https://github.com/yakew7) | Creator and maintainer. Author of the seven audits, the `faircode` library and benchmark harness, the Open Dataset Profiler, the website, and the bulk of the 60 explainers. Code owner for `faircode/`, `paper/`, every `audit.yaml`, and project policy (`CLAUDE.md`, `CONTRIBUTING.md`); co-owner of `explainers/`. |
+| <a href="https://github.com/yakew7"><img src="https://github.com/yakew7.png" width="48" height="48" alt="yakew7"></a> | **Yash Kewlani** - [@yakew7](https://github.com/yakew7) | Creator and maintainer. Author of the seven audits, the `faircode` library and benchmark harness, the Open Dataset Profiler, the website, and the bulk of the 62 explainers. Code owner for `faircode/`, `paper/`, every `audit.yaml`, and project policy (`CLAUDE.md`, `CONTRIBUTING.md`); co-owner of `explainers/`. |
 
 Contact: [yashkewlani2020@gmail.com](mailto:yashkewlani2020@gmail.com) · [@thefaircodeproject](https://instagram.com/thefaircodeproject)
 

METRICS.md

@@ -6,7 +6,7 @@
 ![Contributors](https://img.shields.io/badge/Contributors-29-blue?style=flat-square)
 ![Forks](https://img.shields.io/badge/Forks-33-orange?style=flat-square)
 ![Watching](https://img.shields.io/badge/Watching-8-yellow?style=flat-square)
-![Explainers](https://img.shields.io/badge/Explainers-60-blueviolet?style=flat-square)
+![Explainers](https://img.shields.io/badge/Explainers-62-blueviolet?style=flat-square)
 ![Countries](https://img.shields.io/badge/Countries-20-informational?style=flat-square)
 ![Updated](https://img.shields.io/badge/Updated-Weekly-lightgrey?style=flat-square)
 

README.md

@@ -781,10 +781,10 @@ features = [
 
 ## Explainers
 
-60 short, plain-language write-ups of individual fairness concepts, each with runnable detection code. The healthcare-focused ones are called out above in [Healthcare AI Bias Focus](#healthcare-ai-bias-focus).
+62 short, plain-language write-ups of individual fairness concepts, each with runnable detection code. The healthcare-focused ones are called out above in [Healthcare AI Bias Focus](#healthcare-ai-bias-focus).
 
 <details>
-<summary><strong>Show all 60 explainers →</strong></summary>
+<summary><strong>Show all 62 explainers →</strong></summary>
 
 | Explainer | What it covers |
 |-----------|----------------|
@@ -1205,7 +1205,7 @@ The full public roadmap - with phases, completion status, and content schedule -
 | Combined Social Reach (Instagram + LinkedIn) | 30K+ |
 | Countries Reached (Website Visitors) | 20 |
 | Code Audits Published | 7 |
-| Explainers Published | 60 |
+| Explainers Published | 62 |
 
 Tracked weekly in [METRICS.md](METRICS.md).
 

ROADMAP.md

@@ -44,7 +44,7 @@ Fair Code is an open-source responsible AI platform explaining algorithmic bias,
 
 ## Phase 1 - Bias Glossary and Beginner Explainers ✅
 
-**Status: Foundational library complete - 60 explainers published, expanding toward a 60+ library**
+**Status: Foundational library complete - 62 explainers published, expanding toward a 60+ library**
 
 Build the foundational vocabulary and explain core fairness concepts clearly enough for a non-technical reader.
 

assets/explainers-data.js

@@ -608,5 +608,24 @@ window.FAIR_CODE_EXPLAINERS = [
       "data",
       "metrics"
     ]
+  },
+  {
+    "slug": "maxmin-fairness",
+    "title": "What Is Max-Min (Rawlsian) Fairness?",
+    "subtitle": "Make the worst-off group's outcome as good as possible, even if the groups end up unequal.",
+    "summary": "Learn how max-min (Rawlsian) fairness minimizes the maximum group-level loss instead of equalizing a rate across groups, and why a model can move toward it while still failing demographic parity. Worked on the Audit 03 German Credit data: an iterative group-reweighting loop cuts worst-group error from 29.1% to 27.9% and leaves the 12-point selection-rate gap almost untouched.",
+    "tags": [
+      "metrics"
+    ]
+  },
+  {
+    "slug": "distributionally-robust-optimization",
+    "title": "What Is Distributionally Robust Optimization (DRO) for Fairness?",
+    "subtitle": "Train against the worst subgroup your data might be under-representing, without naming a parity metric.",
+    "summary": "Learn how DRO trains a model to minimize its worst-case loss over an uncertainty set of subgroup distributions - an in-processing alternative to constraint-based methods like ExponentiatedGradient that needs neither a chosen fairness metric nor group labels at prediction time. Worked on the Audit 03 German Credit data: online group DRO reliably shifts training weight onto the worst group, but on a capacity-limited linear model it buys no worst-group generalization - Sagawa et al.'s point about regularization.",
+    "tags": [
+      "metrics",
+      "data"
+    ]
   }
 ];

assets/explainers-data.json

@@ -418,6 +418,20 @@
     "subtitle": "The accuracy cost of a privacy guarantee falls hardest on underrepresented groups.",
     "summary": "Learn how DP-SGD's gradient clipping and noise addition disproportionately degrade accuracy for minority subgroups, so adding a privacy guarantee to a bias-mitigation pipeline is not free. Illustrative example from Bagdasaryan, Poursaeed and Shmatikov (NeurIPS 2019), plus a runnable DP-SGD noise-injection toy; this repo trains no DP model, so no frozen numbers are quoted.",
     "tags": ["data", "metrics"]
+  },
+  {
+    "slug": "maxmin-fairness",
+    "title": "What Is Max-Min (Rawlsian) Fairness?",
+    "subtitle": "Make the worst-off group's outcome as good as possible, even if the groups end up unequal.",
+    "summary": "Learn how max-min (Rawlsian) fairness minimizes the maximum group-level loss instead of equalizing a rate across groups, and why a model can move toward it while still failing demographic parity. Worked on the Audit 03 German Credit data: an iterative group-reweighting loop cuts worst-group error from 29.1% to 27.9% and leaves the 12-point selection-rate gap almost untouched.",
+    "tags": ["metrics"]
+  },
+  {
+    "slug": "distributionally-robust-optimization",
+    "title": "What Is Distributionally Robust Optimization (DRO) for Fairness?",
+    "subtitle": "Train against the worst subgroup your data might be under-representing, without naming a parity metric.",
+    "summary": "Learn how DRO trains a model to minimize its worst-case loss over an uncertainty set of subgroup distributions - an in-processing alternative to constraint-based methods like ExponentiatedGradient that needs neither a chosen fairness metric nor group labels at prediction time. Worked on the Audit 03 German Credit data: online group DRO reliably shifts training weight onto the worst group, but on a capacity-limited linear model it buys no worst-group generalization - Sagawa et al.'s point about regularization.",
+    "tags": ["metrics", "data"]
   }
 ]