This is a continuously growing collection of talks, conferences and texts that I have come across during my ongoing research on smartphone photography and which I consider worth spreading!
In fact exploring smartphone photography culture has lead me to delve into various topics. Starting from a close observation of initially innocent or helpful features - like the automatic creation of albums of those people you have photographed most - I came to explore that there was much more going on in/ via my phone and started asking questions like:
How does image recognition software work? How do Algorithms perceive (visual) data? How intelligent is AI? Why do we believe in the objectivity of machines and numbers - where does this cult around abstract symbols come from? What is the technical infrastructure behind our smartphone communication and who owns what ( content/software/hardware/internet providers/data centers/ wifi hotspots/ electricity masts and undersea cables)? Being my close companion all day round what information do the sensors of my smartphone gather and who gets access to this? ( For example photo/ maps/ health and fitness tracking apps) How do digital companies create user profiles? How do smartphones help match and refine data on online and offline behavior? Why does Google Maps show me exactly this route? Do all the other billions of maps users also follow Google as blindly as I do? What is happening/ will happen to all the data that we share via our phones? How could Big Data benefit citizens rather than commercial or political interests? Being a permanently accessible window to the world, how much has the smartphone already changed and shaped our perception of and interaction with reality?
Even if this site is still under construction I hope you enjoy the thoughts, analyses and critiques I started to gather here. If you like to discuss or share something feel welcome to drop me a line!
Data Sets / Predictive Analytics / Surveillance Capitalism on (training) data collection, user profiles and behavior commodification
The Politics of Images in Machine Learning Training Sets
Kate Crawford and Trevor Paglen open up ImageNet for us - one of the most widely used database of pictures created in order to train AI systems. Here one finds thousands of images: apples and oranges, birds, dogs, horses, mountains, clouds, houses, and street signs. But as you probe further into the dataset things get strange: A photograph of a woman smiling in a bikini is labeled a “slattern, slut, slovenly woman, trollop.” A young man drinking beer is categorized as an “alcoholic, alky, dipsomaniac, boozer, lush,....”
Where did these images come from? What sorts of politics are at work when pictures are paired with labels, and what are the implications when they are used to train technical systems?
Tracking / Datafication
This is an illustrated essay about bodies’ movement traceability, using Michel Foucault’s historical and philosophical method of genealogy. From the scientific domains of archaeology and ethology that particularly focused on animal itineraries, the evolution of traceability technology shifted to the social, capitalist and military realms. Grégoire’s article proofs that the very act of cartographying can never be politically neutral, since it produces a knowledge about a subject, and thus a relationship of power between the cartographer and this same subject.
AI / Algorithmic Governance / Ecological and Social Responsibilities
Digitalisation / Mathematics / Power on cultures of abstraction and our history of believing in numbers
Image Recognition / Algorithmic + Human Perception (How) does AI see and think?