  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 analysing the key
concerns of various AIRTB stakeholders and identify three main dimensions of
trust building in AIRTB, namely security, robustness and fairness. For each of
these dimensions, we propose a unique taxonomy of the state of the art, trace
the root causes of possible breakdown of trust, and discuss the necessity of
the given dimension. This is followed by a comprehensive review of existing
strategies for fulfilling the requirements of each trust dimension. In
addition, we discuss the promising future directions of research essential
towards building trustworthy AIRTB systems to benefit the field of online
advertising.

Information Retrieval;Artificial Intelligence