Looking at Creative ML Blindspots with a Sociological Lens

TL;DR

This paper explores how creative ML and sociology of culture can collaborate by bridging their perspectives on creativity, offering a conceptual toolkit and systematic review to facilitate interdisciplinary research.

Contribution

It introduces a framework combining sociological and ML perspectives on creativity, with a systematic review and toolkit for effective interdisciplinary collaboration.

Findings

Identifies key differences in how ML and social sciences view creativity.

Proposes a conceptual framework based on people, processes, and products.

Provides methodological guidance for cross-disciplinary research.

Abstract

How can researchers from the creative ML/AI community and sociology of culture engage in fruitful collaboration? How do researchers from both fields think (differently) about creativity and the production of creative work? While the ML community considers creativity as a matter of technical expertise and acumen, social scientists have emphasized the role of embeddedness in cultural production. This perspective aims to bridge both disciplines and proposes a conceptual and methodological toolkit for collaboration. We provide a systematic review of recent research in both fields and offer three perspectives around which to structure interdisciplinary research on cultural production: people, processes, and products. We thereby provide necessary grounding work to support multidisciplinary researchers to navigate conceptual and methodological hurdles in their collaboration. Our research will be of interest to ML researchers and sociologists interested in creativity that aim to conduct innovative research by bridging both fields.