Opening the Scope of Openness in AI

TL;DR

This paper explores the concept of openness in AI beyond open source software, proposing a broader, more holistic framework that considers societal, ethical, and disciplinary perspectives.

Contribution

It introduces a taxonomy of openness in AI based on qualitative analysis of 98 concepts, broadening the understanding of openness beyond traditional open source practices.

Findings

Developed a taxonomy of openness in AI

Identified gaps in current openness discussions

Linked openness concepts across disciplines

Abstract

The concept of openness in AI has so far been heavily inspired by the definition and community practice of open source software. This positions openness in AI as having positive connotations; it introduces assumptions of certain advantages, such as collaborative innovation and transparency. However, the practices and benefits of open source software are not fully transferable to AI, which has its own challenges. Framing a notion of openness tailored to AI is crucial to addressing its growing societal implications, risks, and capabilities. We argue that considering the fundamental scope of openness in different disciplines will broaden discussions, introduce important perspectives, and reflect on what openness in AI should mean. Toward this goal, we qualitatively analyze 98 concepts of openness discovered from topic modeling, through which we develop a taxonomy of openness. Using this taxonomy as an instrument, we situate the current discussion on AI openness, identify gaps and highlight links with other disciplines. Our work contributes to the recent efforts in framing openness in AI by reflecting principles and practices of openness beyond open source software and calls for a more holistic view of openness in terms of actions, system properties, and ethical objectives.