mlsanityData to 14 Oct 2026

Shapley Values-Powered Framework for Fair Reward Split in Content Produced by GenAI

arXiv 2403.09700 ↗First posted 5 Mar 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

It is evident that, currently, generative models are surpassed in quality by human professionals. However, with the advancements in Artificial Intelligence, this gap will narrow, leading to scenarios where individuals who have dedicated years of their lives to mastering a skill become obsolete due to their high costs, which are inherently linked to the time they require to complete a task -- a task that AI could accomplish in minutes or seconds. To avoid future social upheavals, we must, even now, contemplate how to fairly assess the contributions of such individuals in training generative models and how to compensate them for the reduction or complete loss of their incomes. In this work, we propose a method to structure collaboration between model developers and data providers. To achieve this, we employ Shapley Values to quantify the contribution of artist(s) in an image generated by the Stable Diffusion-v1.5 model and to equitably allocate the reward among them.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 5 Mar 2024.

Builds on

14 of 26 references.

References · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  6. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  7. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  8. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  9. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  10. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
  11. 2023Extracting Training Data from Diffusion Modelscited by 732
  12. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 629
2 more
  1. 2020Estimating Training Data Influence by Tracing Gradient Descentcited by 580
  2. 2020Influence Functions in Deep Learning Are Fragilecited by 252

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AME: A Multi-Type Contributor Attribution Framework in Generative AI Marketscited by 0

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  1. 2024An Efficient Framework for Crediting Data Contributors of Diffusion Models
  2. 2024GenAI Arena: An Open Evaluation Platform for Generative Models
  3. 2024On the Limitations and Prospects of Machine Unlearning for Generative AI
  4. 2025GenAI Confessions: Black-box Membership Inference for Generative Image Models
  5. 2024Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning
  6. 2025Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value
  7. 2026SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models
  8. 2026Bias at the End of the Score