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On Tuesday, NBC published a story with a gripping headline: “Facial recognition’s ‘dirty little secret’: Millions of online photos scraped without consent.”

The story highlights a recent data set released by IBM with 1 million pictures of faces, intended to help develop fairer face recognition algorithms.

In fact, scraping data from publicly available sources is so much of an industry standard that it’s taught as a foundational skill (sans ethics) in most data science and machine-learning training.Meanwhile, most tech platforms are designed to invite such scraping by offering APIs with direct access to their data.

Rather, this story highlights the need for the tech industry to adapt its cultural norms and standard practices to keep pace with the rapid evolution of the technology itself, as well as the public’s awareness of how their data is used.

In other words, it may have once been a viable practice to blithely scrape people’s data, and it may have once been consent enough for said data to be used as long as it was publicly available, but the advent of AI and the unprecedented scale of Silicon Valley’s data monopolization and monetization have all changed the equation.

IBM Developer Day

more than 40 patents in areas such as

review board and patent review team.

communities as a technical evangelist and thought leader

exchanges, external forums and has been invited in various

on “Watson/IoT” at ICDMAI international conference in Malaysia, Keynote speaker at International Conference on Smart Systems (ICSS 2018) and invited speaker for IoT Summit-Pune 2015,2016 & 2017.He

mentored students which won 3rd prize in Smart India

Use and Fair Use: Statement on shared images in facial recognition AI

Yesterday, NBC News published a story about IBM’s work on improving diversity in facial recognition technology and the dataset that they gathered to further this work.

While we do not have all the facts regarding the IBM dataset, we are aware that fair use allows all types of content to be used freely, and that all types of content are collected and used every day to train and develop AI.

hope we will use this moment to build on the important principles and values of sharing, and engage in discussion with those using our content in objectionable ways, and to speak out on and help shape positive outcomes on the important issues of privacy, surveillance, and AI that impact the sharing of works on the web.

We are taking this opportunity to speak to this particular type of reuse – improving artificial intelligence tools designed for facial recognition through the reuse of content found on the web (not just CC-licensed content) – to help clarify how the licenses work in this context.

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