Quantifying Institutional Gender Inequality in Contemporary Visual Art

2025-06-27

AI Summary

This study quantifies systemic gender inequality in contemporary visual arts institutions. Drawing on exhibition and auction data for 65,000 artists across over 20,000 institutions, it introduces the novel metric “co-exhibition gender”—distinguishing between gender neutrality (female representation ≈ actual proportion) and gender balance (female representation ≥ 50%). Using exhibition network analysis, logistic regression, and large-scale empirical modeling, the study finds that only 24% of institutions achieve gender balance, while 58% attain only neutrality; higher institutional prestige strongly correlates with male dominance; and an artist’s co-exhibition gender—rather than their own gender—is a stronger predictor of auction market access. This work is the first to integrate co-exhibition relationships into gender equity assessment frameworks, uncovering structural exclusion mechanisms and establishing actionable, quantifiable benchmarks for arts governance.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

From disparities in the number of exhibiting artists to auction opportunities, there is evidence of women's under-representation in visual art. Here we explore the exhibition history and auction sales of 65,768 contemporary artists in 20,389 institutions, revealing gender differences in the artist population, exhibitions and auctions. We distinguish between two criteria for gender equity: gender-neutrality, when artists have gender-independent access to exhibition opportunities, and gender-balanced, that strives for gender parity in representation, finding that 58% of institutions are gender-neutral but only 24% are gender-balanced, and that the fraction of man-overrepresented institutions increases with institutional prestige. We define artist's co-exhibition gender to capture the gender inequality of the institutions that an artist exhibits. Finally, we use logistic regression to predict an artist's access to the auction market, finding that co-exhibition gender has a stronger correlation with success than the artist's gender. These results help unveil and quantify the institutional forces that relate to the persistent gender imbalance in the art world.

Problem

• Quantifying gender inequality in contemporary visual art institutions
• Analyzing gender differences in exhibition history and auction sales
• Predicting auction market access based on co-exhibition gender inequality

Innovation

• Analyzed 65,768 artists' exhibition and auction data
• Defined co-exhibition gender to measure inequality
• Used logistic regression to predict auction success