Exploring Tokenized Incentive Models for AI Contributions in Blockchain Ecosystems

Authors

  • Dr. Emily Thompson Assistant Professor, Department of Computer Science, University of California, Berkeley, USA Author

Keywords:

Tokenization, Blockchain, Artificial Intelligence, Incentive Models, Fair Compensation, Decentralized Systems

Abstract

The integration of artificial intelligence (AI) with blockchain technology presents innovative opportunities to enhance collaborative development and fair compensation models. This paper investigates tokenized incentive models designed to reward contributions in AI development within blockchain ecosystems. Tokenization enables a decentralized method of valuing and compensating individual contributions, thereby fostering a collaborative environment that encourages the sharing of knowledge and expertise. This research explores how these models can effectively balance incentives between contributors, maintain transparency, and promote long-term engagement in AI projects. It also discusses potential challenges in implementing these incentive systems, including governance, market volatility, and regulatory concerns. The findings highlight the necessity of creating robust frameworks that ensure equitable compensation while addressing the complexities inherent in both AI and blockchain domains.

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Published

08-10-2024

How to Cite

[1]
Dr. Emily Thompson, “Exploring Tokenized Incentive Models for AI Contributions in Blockchain Ecosystems”, Distrib Learn Broad Appl Sci Res, vol. 10, pp. 339–344, Oct. 2024, Accessed: Dec. 03, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/160

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