We initiate a principled study of algorithmic collective action on digital platforms that deploy
machine learning algorithms. We propose a simple theoretical model of a collective …

Many powerful computing technologies rely on implicit and explicit data contributions from
the public. This dependency suggests a potential source of leverage for the public in its …

A growing body of work has highlighted the important role that Wikipedia's volunteer-created
content plays in helping search engines achieve their core goal of addressing the …

A large body of research has attempted to ensure that algorithmic systems adhere to notions
of fairness and transparency. Increasingly, researchers have highlighted that mitigating …

The rapid adoption of facial recognition (FR) technology by both government and
commercial entities in recent years has raised concerns about civil liberties and privacy. In …

Data portability--the capability to transfer one's data from one platform to another--has been
described as an important tool for giving individuals more control over their data. It is defined …

We investigate algorithmic collective action in transformer-based recommender systems.
Our use case is a collective of fans aiming to promote the visibility of an artist by strategically …

Classical machine learning and statistics are built on the paradigm that there is a fixed
quantity that we want to learn about a population, such as the best predictor of outcomes …

Many computing technologies are primarily useful because of the existence of some set of
data created by people, intentionally in some cases and unintentionally in others. For …

As machine learning (ML) models have grown in size and scope in recent years, so has the
amount of data needed to train them. Unfortunately, individuals whose data is used in large …