AI IN MANAGING ONLINE ART AUCTIONS - Samrat, Bhavuk; Bindhu M C,; Jacob, T. Prem; Kumar, Prince; John, Reginold; Dhawan, Aashim (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The traditional systems of auction that used to rely on manual skills and face-to-face interaction have shifted to hybrid and fully digital frameworks that use AI to become efficient and accurate. The algorithms used in machine learning are Decision Tree Regression and Support Vector Regression (SVR) to determine the optimal pricing basing on the past sales records, reputation of the artist and the market. Computer vision tools can help in recognizing and authenticating artworks accurately, which would curb the fraud and guarantee provenance verification. Bidder-auction platform communication is personalized and intelligent with natural language processing (NLP) models, including rule-based chatbots and transformer-based models. Another way AI can streamline the management of operations is by using automated registration and verification, automated matchmaking between buyers and works of art, and the establishment of smart bidding and dynamic pricing. The paper also examines the complex effects of AI on the most important stakeholders, who are artists, galleries, collectors, and auctioneers, and identifies the benefits of transparency, accessibility, and satisfaction on the side of users.