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 (PAGE UNDER DEVELOPMENT)
 
-<p><div style="font-size:20px;line-height:36px">Ever since government agencies began developing face recognition in the early 1960's, datasets of face images have always been central to the development and evaluation face recognition technology. Today, these datasets no longer originate in labs, but instead from family photo albums posted on social media sites, CCTV camera footage from college campuses, search engine queries for celebrities, cafe livestreams, or <a href="https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset">videos on YouTube</a>. </div></p>
+<p><div style="font-size:20px;line-height:36px">Ever since government agencies began developing face recognition in the early 1960's, datasets of face images have always been central to the development and evaluation face recognition technology. Today, these datasets no longer originate in labs, but instead from family photo albums posted on photo sharing sites, surveillance camera footage from college campuses, search engine queries for celebrities, cafe livestreams, or <a href="https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset">videos on YouTube</a>. </div></p>
 
 While many of these datasets include public figures such as politicians, athletes, and actors; they also include many non-public figures including digital activists, students, pedestrians, and people's semi-private shared photo albums. Some images are used with creative commons licenses, yet others were taken in unconstrained scenarios without awareness or consent. At first glance it appears many of the datasets were created for seemingly harmless academic research, but when examined further it becomes clear that they're also used by foreign defense agencies.
 
-During the last year, hundreds of these facial analysis datasets created "in the wild" have been collected to understand how they contribute to a global supply chain of biometric data that is helping to power the global facial recognition industry.
+During the last year, hundreds of these facial analysis datasets created "in the wild" have been collected to understand how they contribute to a global supply chain of biometric data that is powering the global facial recognition industry.
 
-MegaPixels is art and research by <a href="https://ahprojects.com">Adam Harvey</a> about publicly available facial recognition datasets that aims to unravel their histories, futures, geographies, and contents. Throughout 2019 this site, coded by Jules LaPlace, will publish research reports, visualizations, downloadable statistics, and interactive tools for searching the datasets.
+MegaPixels is art and research by <a href="https://ahprojects.com">Adam Harvey</a> about publicly available facial recognition datasets that aims to unravel their histories, futures, geographies, and context. Throughout 2019 this site, coded and designed by Jules LaPlace, will publish research reports, visualizations, downloadable statistics, and interactive tools for searching the datasets.
 
-The MegaPixels website is produced in partnership with [Mozilla](https://mozilla.org) who provided the funding to research the datasets, build the site, and develop tools to help you understand the role these datasets have played in creating biometric surveillance technologies. 
+The MegaPixels website is produced in partnership with [Mozilla](https://mozilla.org) who provided funding to research the datasets, build the site, and develop tools to help you understand the role these datasets have played in creating biometric surveillance technologies. 
 
-The MegaPixels site is based on an [earlier installation](https://ahprojects.com/megapixels-glassroom), also supported by Mozilla, at the [Tactical Tech Glassroom](https://theglassroom.org/) in London about the facial recognition datasets; and a commission from the Elevate arts festival curated by Berit Gilma about pedestrian recognition datasets.
+The MegaPixels site is based on an earlier [installation](https://ahprojects.com/megapixels-glassroom) (also supported by Mozilla) at the [Tactical Tech Glassroom](https://theglassroom.org/) in London in 2017; and a commission from the Elevate arts festival curated by Berit Gilma about pedestrian recognition datasets in 2018, and research during [CV Dazzle](https://cvdazzle.com) from 2010-2015. Through the many prototypes, conversations, pitches, PDFs, and false starts this project has endured during the last 5 years, it eventually evolved into something much different than originally imagined. Now, as datasets become increasingly influential in shaping the computational future, it's clear that they must be critically analyzed to understand the biases, shortcomings, funding sources, and contributions to the surveillance industry. However, it's misguided to only criticize these datasets for their flaws without also praising their contribution to society. Without publicly available facial analysis datasets there would be less public discourse, less open-source software, and less peer-reviewed research. Public datasets can indeed become a vital public good for the information economy but as this projects aims to illustrate, many ethical questions arise about consent, intellecture property, surveillance, and privacy.
 
 
 ### MegaPixels Team