This week NBC published an article exploring the source of training data sets for facial recognition. It makes the claim that we ourselves are providing, without consent, the data that may well be used to put us under surveillance.

In January IBM made a database available for research into facial recognition algorithms. The database contains some 1 million face descriptions that can be used as a training set. Called “Diversity in Faces” the stated aim is to reduce bias in current facial recognition abilities. Such bias is rampant often due to too small and too heterogenous (compared to the global population) data sets used in training. That stated goal is ethically sound it seems, but the means used to get there raises a few questions with me. Specifically if the means live up to the same ethical standards that IBM says it seeks to attain with the result of their work. This and the next post explore the origins of the DiF data, my presence in it, and the questions it raises to me.

What did IBM collect in “Diversity in Faces”?
Let’s look at what the data is first. Flickr is a photo sharing site, launched in 2004, that started supporting publishing photos with a Creative Commons license from early on. In 2014 a team led by Bart Thomee at Yahoo, which then owned Flickr, created a database of 100 million photos and videos with any type of Creative Commons license published in previous years on Flickr. This database is available for research purposes and known as the ‘YFCC-100M’ dataset. It does not contain the actual photos or videos per se, but the static metadata for those photos and videos (urls to the image, user id’s, geo locations, descriptions, tags etc.) and the Creative Commons license it was released under. See the video below published at the time:

YFCC100M: The New Data in Multimedia Research from CACM on Vimeo.

IBM used this YFCC-100M data set as a basis, and selected 1 million of the photos in it to build a large collection of human faces. It does not contain the actual photos, but the metadata of that photo, and a large range of some 200 additional attributes describing the faces in those photos, including measurements and skin tones. Where YFC-100M was meant to train more or less any image recognition algorithm, IBM’s derivative subset focuses on faces. IBM describes the dataset in their Terms of Service as:

a list of links (URLs) of Flickr images that are publicly available under certain Creative Commons Licenses (CCLs) and that are listed on the YFCC100M dataset (List of URLs together with coding schemes aimed to provide objective measures of human faces, such as cranio-facial features, as well as subjective annotations, such as human-labeled annotation predictions of age and gender(“Coding Schemes Annotations”). The Coding Schemes Annotations are attached to each URL entry.

My photos are in IBM’s DiF
NBC, in their above mentioned reporting on IBM’s DiF database, provide a little tool to determine if photos you published on Flickr are in the database. I am an intensive user of Flickr since early 2005, and published over 25.000 photos there. A large number of those carry a Creative Commons license, BY-NC-SA, meaning that as long as you attribute me, don’t use an image commercially and share your result under the same license you’re allowed to use my photos. As the YFCC-100M covers the years 2004-2014 and I published images for most of those years, it was likely my photos are in it, and by extension likely my photos are in IBM’s DiF. Using NBC’s tool, based on my user name, it turns out 68 of my photos are in IBM’s DiF data set.

One set of photos that apparently is in IBM’s DiF cover the BlogTalk Reloaded conference in Vienna in 2006. There I made various photos of participants and speakers. The NBC tool I mentioned provides one photo from that set as an example:

Thomas Burg

My face is likely in IBM’s DiF
Although IBM doesn’t allow a public check who is in their database, it is very likely that my face is in it. There is a half-way functional way to explore the YFCC-100M database, and DiF is derived from the YFCC-100M. It is reasonable to assume that faces that can be found in YFCC-100M are to be found in IBM’s DiF. The German university of Kaiserslautern at the time created a browser for the YFCC-100M database. Judging by some tests it is far from complete in the results it shows (for instance if I search for my Flickr user name it shows results that don’t contain the example image above and the total number of results is lower than the number of my photos in IBM’s DiF) Using that same browser to search for my name, and for Flickr user names that are likely to have taken pictures of me during the mentioned BlogTalk conference and other conferences, show that there is indeed a number of pictures of my face in YFCC-100M. Although the limited search in IBM’s DiF possible with NBC’s tool doesn’t return any telling results for those Flickr user names. it is very likely my face is in IBM’s DiF therefore. I do find a number of pictures of friends and peers in IBM’s DiF that way, taken at the same time as pictures of myself.

Photos of me in YFCC-100M

But IBM won’t tell you
IBM is disingenuous when it comes to being transparent about what is in their DiF data. Their TOS allows anyone whose Flickr images have been incorporated to request to be excluded from now on, but only if you can provide the exact URLs of the images you want excluded. That is only possible if you can verify what is in their data, but there is no public way to do so, and only university affiliated researchers can request access to the data by stating their research interest. Requests can be denied. Their TOS says:

3.2.4. Upon request from IBM or from any person who has rights to or is the subject of certain images, Licensee shall delete and cease use of images specified in such request.

Time to explore the questions this raises
Now that the context of this data set is clear, in a next posting we can take a closer look at the practical, legal and ethical questions this raises.

Wenn du schreibst, Heinz, das der Studiengang Content Strategie noch aktiv in Entwicklung ist, da die Disziplin sich noch immer weiter gestaltet, heisst das denn das ihr euch zunehmend auch mit Algorithmen usw auseinandersetzt? Nebst zB Einflussnahme auf Wahlen, wobei ich Inhalte gezeigt bekomme die andere nicht über den selben Politiker zu sehen bekommen, fand ich gestern ein krasses Beispiel wobei auf Netflix andere Akteure im ‘Filmplakat’ gezeigt werden je nach meinem Profil, inklusive meiner Hautfarbe. Die Frage dabei ist wohl wann eine Strategie zum ‘dark pattern‘ wird. Und wann es unendlich leichter ist mich was vorzuzeigen als für mich mich dagegen zu wehren. Machtdifferenzen durch Content Strategie?

Wired is calling for an RSS revival.

RSS is the most important piece of internet plumbing for following new content from a wide range of sources. It allows you to download new updates from your favourite sites automatically and read them at your leisure. Dave Winer, forever dedicated to the open web, created it.

I used to be a very heavy RSS user. I tracked hundreds of sources on a daily basis. Not as news but as a way to stay informed about the activities and thoughts of people I was interested in. At some point, that stopped working. Popular RSS readers were discontinued, most notably Google’s RSS reader, many people migrated to the Facebook timeline, platforms like Twitter stopped providing RSS feeds to make you visit their platform, and many people stopped blogging. But with FB in the spotlight, there is some interest in refocusing on the open web, and with it on RSS.

Currently I am repopulating from scratch my RSS reading ‘antenna’, following around 100 people again.

Wired in its call for an RSS revival suggests a few RSS readers. I, as I always have, use a desktop RSS reader, which currently is ReadKit. The FB timeline presents stuff to you based on their algorithmic decisions. As mentioned I definitely would like to have smarter ways of shaping my own information diet, but then with me in control and not the one being commoditised.

So it’s good to read that RSS Reader builders are looking at precisely that.
“Machines can have a big role in helping understand the information, so algorithms can be very useful, but for that they have to be transparent and the user has to feel in control. What’s missing today with the black-box algorithms is where they look over your shoulder, and don’t trust you to be able to tell what’s right.”,says Edwin Khodabakchian cofounder and CEO of RSS reader Feedly (which currently has 14 million users). That is more or less precisely my reasoning as well.

Stephanie Booth, a long time blogging connection, has been writing about reducing her Facebook usage and increasing her blogging. She says at one point

As the current “delete Facebook” wave hits, I wonder if there will be any kind of rolling back, at any time, to a less algorithmic way to access information, and people. Algorithms came to help us deal with scale. I’ve long said that the advantage of communication and connection in the digital world is scale. But how much is too much?

I very much still believe there’s no such thing as information overload, and fully agree with Stephanie that the possible scale of networks and connections is one of the key affordances of our digital world. My rss-based filtering, as described in 2005, worked better when dealing with more information, than with less. Our information strategies need to reflect and be part of the underlying complexity of our lives.

Algorithms can help us with that scale, just not the algorithms that FB uses around us. For algorithms to help, like any tool, they need to be ‘smaller’ than us, as I wrote in my networked agency manifesto. We need to be able to control its settings, tinker with it, deploy it and stop it as we see fit. The current application of algorithms, as they usually need lots of data to perform, sort of demands a centralised platform like FB to work. The algorithms that really will be helping us scale will be the ones we can use for our own particular scaling needs. For that the creation, maintenance and usage of algorithms needs to have a much lower threshold than now. I placed it in my ‘agency map‘ because of it.

Going back to a less algorithmic way of dealing with information isn’t an option, nor something to desire I think. But we do need algorithms that really serve us, perform to our information needs. We need less algorithms that purport to aid us in dealing with the daily river of newsy stuff, but really commodotise us at the back-end.