Navigating Authorship and Ethics: A Framework for Evaluating Human-AI Collaborative Outputs in Art Education

Abstract

The marginalization of critical AI research—which examines the social, ethical, and political implications of algorithmic systems—is not a reflection of its intellectual rigor, but rather a result of the structural and economic forces that define the current AI landscape. Critical AI research often acts as the "single, high-quality voice" attempting to correct a powerful, well-funded "consensus" of techno-optimism.
Structural Asymmetry in Research Funding
The primary driver of the AI research landscape is the immense capital required for large-scale model development. This creates a "funding-driven paradigm" where research that advances capability is prioritized over research that questions the societal cost.
Corporate Capture of Talent. A significant portion of AI PhDs are recruited by "Big Tech" firms. According to the AI Index Report (2023), the number of AI PhDs entering industry (approximately 70%) significantly outpaces those entering academia (approximately 20%).
The Incentive Gap. Research that improves model efficiency or accuracy has a direct Return on Investment (ROI). Conversely, critical research—such as audits of algorithmic bias or environmental impact studies—often presents a legal or reputational risk to the funders (Metcalf & Crawford, 2016).