Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning
Xiaoli Tang, Han Yu

TL;DR
This paper provides a comprehensive survey of trustworthy AI-empowered real-time bidding systems in online advertising, analyzing key trust concerns and reviewing current strategies to enhance security, robustness, and fairness.
Contribution
It offers the first detailed taxonomy and analysis of trust dimensions in AIRTB, bridging a gap in the literature and guiding future research directions.
Findings
Identifies security, robustness, and fairness as key trust dimensions.
Provides a taxonomy of state-of-the-art strategies for trust building.
Discusses future research directions for trustworthy AIRTB.
Abstract
Artificial intelligence-empowred Real-Time Bidding (AIRTB) is regarded as one of the most enabling technologies for online advertising. It has attracted significant research attention from diverse fields such as pattern recognition, game theory and mechanism design. Despite of its remarkable development and deployment, the AIRTB system can sometimes harm the interest of its participants (e.g., depleting the advertisers' budget with various kinds of fraud). As such, building trustworthy AIRTB auctioning systems has emerged as an important direction of research in this field in recent years. Due to the highly interdisciplinary nature of this field and a lack of a comprehensive survey, it is a challenge for researchers to enter this field and contribute towards building trustworthy AIRTB technologies. This paper bridges this important gap in trustworthy AIRTB literature. We start by…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Auction Theory and Applications · Ethics and Social Impacts of AI
