Alek Tarkowski

Introducing the concept of generative interoperability - a design principle that has the potential to build a more decentralized infrastructure that enables individual self-determination in the online environment.

In today’s digital environment, openness serves as both a challenge to concentrations of power and its enabler. Solving this paradox is at the heart of our work, which focuses on three objectives.

This is a preprint of a book chapter, to be published in: Alexander Baratsits (ed.), European Public Spheres, Digitisation and Public Welfare Orientation.

These notes offer an initial analysis of a growing movement that strives to make key machine learning tools and technologies open and shared.

A white paper on understanding the implications of face recognition training with CC-licensed photographs

In this document, we collect questions delineating the field of research and activism that we refer to as "Openness and AI."

Research report based on interviews with digital activists and open movement leaders

One of our goals at Open Future is to make sense of openness and the movement advancing this principle. To this end, we have conducted an exploratory mapping of the movement using network analysis methods and data collected from Twitter.

This white paper proposes an approach to sharing data sets for AI training as a public good, governed as a commons.

This paper, which has been informed by a series of workshop discussions, maps possible paths to building a books data commons and outlines key questions relevant to developers, repositories, and other potential stakeholders.

This report captures learnings from the Alignment Assembly on AI and the Commons, a six-week online deliberation of open movement activists, creators, and organizations about regulating generative AI.