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@@ -49,8 +49,11 @@
   </div><div class='meta'>
     <div class='gray'>Website</div>
     <div><a href='http://www.msceleb.org/' target='_blank' rel='nofollow noopener'>msceleb.org</a></div>
-  </div></div><p><a href="https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology">https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology</a></p>
-<p><a href="https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm">https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm</a></p>
+  </div></div><p>The Microsoft Celeb dataset is a face recognition training site made entirely  of images scraped from the Internet. According to Microsoft Research who created and published the dataset in 2016, MS Celeb is the largest publicly available face recognition dataset in the world, containing over 10 million images of 100,000 individuals.</p>
+<p>But Microsoft's ambition was bigger. They wanted to recognize 1 million individuals. As part of their dataset they released a list of 1 million target identities for researchers to identity. The identities</p>
+<p><a href="https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/">https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/</a></p>
+<p>In 2019, Microsoft CEO Brad Smith called for the governmental regulation of face recognition, an admission of his own company's inability to control their surveillance-driven business model. Yet since then, and for the last 4 years, Microsoft has willingly and actively played a significant role in accelerating growth in the very same industry they called for the government to regulate. This investigation looks look into the <a href="https://www.microsoft.com/en-us/research/publication/ms-celeb-1m-dataset-benchmark-large-scale-face-recognition-2/">MS Celeb</a> dataset and Microsoft Research's role in creating and distributing the largest publicly available face recognition dataset in the world to both.</p>
+<p>to spur growth and incentivize researchers, Microsoft released a dataset called <a href="https://msceleb.org">MS Celeb</a>, or Microsft Celeb, in which they developed and published a list of exactly 1 million targeted people whose biometrics would go on to build</p>
 </section><section>
   <h3>Who used Microsoft Celeb?</h3>
 
@@ -98,7 +101,7 @@
 
   <h3>Dataset Citations</h3>
   <p>
-    The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers.  Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources.  These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms.
+    The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers.  Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources.  These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. If you use our data, please <a href="/about/attribution">cite our work</a>.
   </p>
 
   <div class="applet" data-payload="{&quot;command&quot;: &quot;citations&quot;}"></div>
@@ -113,11 +116,12 @@
   
 </section><section><h3>Additional Information</h3>
 <ul>
-<li>The dataset author spoke about his research at the CVPR conference in 2016 <a href="https://www.youtube.com/watch?v=Nl2fBKxwusQ">https://www.youtube.com/watch?v=Nl2fBKxwusQ</a></li>
+<li>SenseTime <a href="https://www.semanticscholar.org/paper/The-Devil-of-Face-Recognition-is-in-the-Noise-Wang-Chen/9e31e77f9543ab42474ba4e9330676e18c242e72">https://www.semanticscholar.org/paper/The-Devil-of-Face-Recognition-is-in-the-Noise-Wang-Chen/9e31e77f9543ab42474ba4e9330676e18c242e72</a></li>
+<li>Microsoft used it <a href="https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70">https://www.semanticscholar.org/paper/One-shot-Face-Recognition-by-Promoting-Classes-Guo/6cacda04a541d251e8221d70ac61fda88fb61a70</a></li>
+<li><a href="https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology">https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology</a></li>
+<li><a href="https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm">https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm</a></li>
 </ul>
-</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^readme]" class="footnote_shim"></a><span class="backlinks"></span><p>"readme.txt" <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">https://exhibits.stanford.edu/data/catalog/sx925dc9385</a>.</p>
-</li><li><a name="[^localized_region_context]" class="footnote_shim"></a><span class="backlinks"></span><p>Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.</p>
-</li><li><a name="[^replacement_algorithm]" class="footnote_shim"></a><span class="backlinks"></span><p>Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.</p>
+</section><section><h3>References</h3><section><ul class="footnotes"><li><a name="[^brad_smith]" class="footnote_shim"></a><span class="backlinks"></span><p>Brad Smith cite</p>
 </li></ul></section></section>
 
   </div>