Networked Labor

Workshop by Michael Murtaugh and Nicolas Malevé during HDSA2016

The point of departure of the Networked Labor was the connection between a certain form of networked labor and the kind of software and collective intelligence that is currently developed. A concrete example is how Amazon Mechanical Turk workers are contributing to machine learning. Millions of manual annotations are necessary for a state of the art algorithm to learn. Without such a massive effort, such a technique would be pointless. However, to collect such an amount of training data would be much too costly if workers were hired under traditional labor conditions.
AMT provides a mechanism of blunt exploitation, - the task is paid a few cents, and of extreme mechanization, - workers are responding to api calls.


From presentation: Retinas' Routines


From presentation: Retinas' Routines


From presentation: Retinas' Routines

Participants of the Hackers & Designers Summer Academy were invited to map out the different economical processes at play in the creation of a machine learning algorithm, to unfold the networks of people, machines and resources that are necessary for it to operate, and test various alternative scenarios. How can we learn and work together?

Concretely, we worked at different levels and between technical experimentation and more theoretical discussions.

Dividing a task in discrete units of work that can be performed individually is like writing an algorithm, but also like writing a score. This has huge consequences as it defines the kind of perception and intelligence that feeds machine learning and feeds back in our devices and systems. How can we perform such tasks in a reflexive manner?

Face training excercises:


Neural face training

Face training

Face recognition

Trump training

Face training
Smile recognition

Notes, references and code bits from the workshop here: