# Survey of Artificial Intelligence Model Marketplace
## 6 Challenges and Future Directions  
Our review reveals that most available AI trading markets follow the structure of application marketplaces. These platforms focus on listing various AI models with different categories, leaving the burden on customers to independently select suitable models without providing adequate guidance or support. Representative efforts target the realization of ideal AI marketplaces, such as auctions in distributed (federated learning services) market settings [88; 115], performance-based pricing in centralized market settings [116; 117], to enhance their structure and functionality. However, challenges in the following areas require the research community's attention. * _Lack of Standardization:_ AI models often lack uniform templates or formats, and the use of diverse datasets for training further complicates their integration and reuse across different platforms. An ideal AI marketplace should address this issue by standardizing feature descriptions, listing comparable attributes across AI models, and providing users with tailored suggestions to facilitate informed decision-making. * _Price Efficiency:_ Many platforms leave price information undisclosed, requiring customers to contact developers directly for demos and pricing details. This approach lacks transparency and shifts the burden onto sellers, who may struggle to determine appropriate pricing, leading to undervalued or overpriced. In addition, it provides risks that customers might contact the developers directly, bypassing the AI marketplace, and the marketplace is only treated as the information provider rather than a platform to control and monitor the trade of the AI model. An ideal AI marketplace should offer price recommendations, ensuring maximum price efficiency while safeguarding the interests of both sellers and buyers and developing loyal customers and suppliers. Several studies have explored pricing efficiency in this context, such as performance-based auctions in the marketplaces to maximize seller's profit. However, these efforts are still in their theoretical conceptual stage, which needs to be transited to fruition to prove their efficiency. * _Policy:_ Policies related to AI models, such as intellectual property protection, as well as industry-specific regulations like HIPAA in the healthcare sector, must be effectively regulated and enforced by the AI marketplace. This oversight is essential to ensure that all AI models sold on the platform comply with applicable legal and ethical standards, therefore safeguarding both developers and end-users [118]. * _Ease of Operation:_ Many existing platforms categorize AI products based on technical aspects, such as deep-learning frameworks, rather than industry-specific applications. Some AI marketplaces provide user cases for customers to refer to but lack demo samples for customers to try. This approach creates significant challenges for non-technical customers who may lack expertise in AI model development, making it difficult for them to identify and select appropriate solutions for their needs. * _Privacy:_ To promote AI models, some AI platforms offer free trials, allowing customers to evaluate performance before making a purchase decision. However, these free trials often lack adequate protection measures. Users can gain access to these demos by providing fake information, such as an email address or company details, and then implement the models on their own computers. This creates risks, including the unauthorized distribution of samples to third parties or the replication of demomodels, which cannot be effectively monitored or controlled. How to find a balance point between AI model promotion and AI model protection will be a challenging task for the AI marketplace to consider. The ideal situation should be to provide an easy way for customers to try the AI model in the AI marketplace platform rather than download the AI model to their computers.