Art - Creativity
In this cluster, concerns about negative impacts on human creativity, particularly through text-to-image models, are prevalent. Papers criticize financial harms or economic losses for artists due to the widespread generation of synthetic art as well as the unauthorized and uncompensated use of artists' works in training datasets. Additionally, given the challenge of distinguishing synthetic images from authentic ones, there is a call for systematically disclosing the non-human origin of such content, particularly through watermarking. Moreover, while some sources argue that text-to-image models lack 'true' creativity or the ability to produce genuinely innovative aesthetics, others point out positive aspects regarding the acceleration of human creativity.
ENTITY
1 - Human
INTENT
1 - Intentional
TIMING
2 - Post-deployment
Risk ID
mit82
Domain lineage
6. Socioeconomic and Environmental
6.3 > Economic and cultural devaluation of human effort
Mitigation strategy
1. Establish a mandated, auditable compensation framework for generative AI model training, utilizing mechanisms such as collective licensing agreements or a pay-per-use royalty system, to ensure equitable financial remuneration for artists whose copyrighted works contribute to the aesthetic capability of the models. 2. Mandate the systematic integration of content provenance measures—including robust, non-removable digital watermarking and visible content labels—at the model-deployment level to unequivocally disclose the artificial or non-human origin of synthetic media. 3. Advance legislative and regulatory clarity on the application of copyright law to Generative AI, specifically by defining the bounds of the Fair Use doctrine concerning training data ingestion and establishing clear criteria for the minimum human authorial contribution required for copyright protection of AI-assisted works.