Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning

AI-powered ad auctions are vulnerable to fraud that directly harms advertisers: The paper identifies that Real-Time Bidding (RTB) systems can "deplete the advertisers' budget with various kinds of fraud," highlighting a critical gap between the technology's promise and its current trustworthiness in protecting participant interests.

Trust in AI advertising requires three interconnected safeguards: The authors establish that building trustworthy AI-driven bidding systems depends on three dimensions—security, robustness, and fairness—suggesting that addressing only one aspect (like security) is insufficient for comprehensive protection of all stakeholders.

Multiple stakeholders have competing trust concerns in ad auctions: The paper emphasizes analyzing "key concerns of various AIRTB stakeholders," indicating that advertisers, publishers, platforms, and users may have different vulnerabilities and needs, requiring tailored approaches rather than one-size-fits-all solutions.

The field lacks unified frameworks for trustworthiness: The authors note "a lack of a comprehensive survey" and describe their work as bridging "an important gap in trustworthy AIRTB literature," suggesting that the responsible AI community has not yet established clear standards or shared understanding of what makes ad auction systems trustworthy.

Root cause analysis is essential for building trust: The paper commits to "tracing the root causes of possible breakdown of trust" for each dimension, implying that effective safeguards require understanding why systems fail, not just implementing surface-level protections.