Objectives Fairness is a core concept meant to grapple with different forms of discrimination
and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet …

A growing body of literature has proposed formal approaches to audit algorithmic systems
for biased and harmful behaviors. While formal auditing approaches have been greatly …

The field of digital mental health is making strides in the application of technology to
broaden access to care. We critically examine how these technology-mediated forms of care …

Many recent technological advances (eg ChatGPT and search engines) are possible only
because of massive amounts of user-generated data produced through user interactions …

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 …

With the growing reliance on artificial intelligence (AI) for many different applications, the
sharing of code, data, and models is important to ensure the replicability and …

The increasing prevalence of large-scale labor aggregation platforms, worker analytics, and
algorithmic decision-making by management raises the question of whether workers can …

Online volunteers are a crucial labor force that keeps many for-profit systems afloat (eg
social media platforms and online review sites). Despite their substantial role in upholding …

Amidst calls for public accountability over large data-driven systems, feminist and
indigenous scholars have developed refusal as a practice that challenges the authority of …

In recent years, there has been a growing interest in the field of “AI Ethics” and related areas.
This field is purposefully broad, allowing for the intersection of numerous subfields and …