Potential digital transformation of the art market

: the application of AI machine learning to art authentication

Abstract

This thesis explores Machine Learning (ML) for authenticating art. Interpol treats art forgery as a serious crime with the number of forgeries threatening the growing field of art as an investment asset class. Present authentication methods appear to lack efficacy, reducing their ability to address this challenge. The literature review outlines the current state of art authentication, ML and digital transformation. A questionnaire explored the current state of authentication, the diffusion status of AI art authentication as per Rogers (2003), its disruptive potential according Si & Chen (2020), and its potential acceptance according to Venkatesh and Bala (2008). Responses from 65 participants were collected. The findings revealed a growing demand for AI art authentication, with the art market showing awareness and preliminary adoption but not full implementation. Although currently not a disruptive technology, ML has the potential to complement traditional authentication methods but will face acceptance resistance that can be mitigated through strategic interventions. According Hanelt et al's (2020) framework, AI art authentication has the potential to digitally transform the artworld. Conflict of interest: The author is associated with ARTTRD, a company involved in AI art authentication. Despite this, efforts have been made to maintain academic integrity and prevent bias in the research due to this affiliation.

Date of Award	31 Jan 2024
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Peter V. Rajsingh (Supervisor)