Showing posts from November, 2019

Private Enough

I recently attended a discussion of The Smart Enough City , which got me thinking about what "private enough" online services might mean to people. Privacy is an admittedly slippery concept and your idea of privacy may differ dramatically from mine. Privacy as "contextual integrity"  is one concept that helps address the definitional inconsistencies by focusing on information transfer . However, the scholarly literature which I have great respect for won't be particularly useful in explaining what I'm working on to my relatives at Thanksgiving dinner. A look at some everyday online activities will demonstrate how the battle to make the Internet private enough is coming from many directions. The first elephant in the room is online advertising, which is something I'm not entirely opposed to. It's just the undisclosed third party data sharing without anything that feels like meaningful opt out that’s too invasive. My views lean towards those a

Thinking About BIPA and Machine Learning

One article that really caught my attention recently discussed the use of Creative Commons-licensed images from Flickr as part of the MegaFace dataset for training facial recognition algorithms. Despite its aggressive (but not untrue) title, it highlights the many sides of the questions we the people and we the companies building products with these technologies face confront. Focusing on the licensing, Flickr truly expanded the available commons of openly-licensed images by allowing its community to choose Creative Commons (CC) licenses. Interestingly, the latest version of the most permissive CC license expressly does not license "publicity, privacy, and/or other similar personality rights", yet the licensor agrees not to assert such rights to the extent necessary to support the rest of the license. However, previous versions of this or other CC licenses probably apply to many photos in the data set, and not all of the other licenses contain this language. For the Cre