# Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning arXiv: https://arxiv.org/abs/2210.07770 ยท HTML: https://mlsanity.com/paper/2210.07770/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 40 of its 182 references that are in the map. - First posted on arXiv 21 Sep 2022. ## Builds on (most cited first, 30 of 40) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017) - [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020) - [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020) - [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018) - [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020) - [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017) - [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://mlsanity.com/paper/1912.11078/) (2019) - [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018) - [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022) - [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021) - [Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination](https://mlsanity.com/paper/1408.6491/) (2014) - [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020) - [Towards Long-term Fairness in Recommendation](https://mlsanity.com/paper/2101.03584/) (2021) - [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021) - [How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness](https://mlsanity.com/paper/1811.03654/) (2018) - [A Survey on Ensemble Learning under the Era of Deep Learning](https://mlsanity.com/paper/2101.08387/) (2021) - [Fairness Under Composition](https://mlsanity.com/paper/1806.06122/) (2018) - [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016) - [Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning](https://mlsanity.com/paper/1910.09337/) (2019) - [Byzantine-resilient Decentralized Stochastic Gradient Descent](https://mlsanity.com/paper/2002.08569/) (2020) - [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021) - [Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction](https://mlsanity.com/paper/2104.09713/) (2021) - [ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning](https://mlsanity.com/paper/2010.06398/) (2020) - [Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints](https://mlsanity.com/paper/2106.08652/) (2021) - [Auditing for Discrimination in Algorithms Delivering Job Ads](https://mlsanity.com/paper/2104.04502/) (2021) - [Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes](https://mlsanity.com/paper/1904.02095/) (2019) - [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021) - [Deep Landscape Forecasting for Real-time Bidding Advertising](https://mlsanity.com/paper/1905.03028/) (2019) ## Built on by (most cited first, 4 of 4) - [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025) - [FairJob: A Real-World Dataset for Fairness in Online Systems](https://mlsanity.com/paper/2407.03059/) (2024) - [Intelligent Agents for Auction-based Federated Learning: A Survey](https://mlsanity.com/paper/2404.13244/) (2024) - [A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineering](https://mlsanity.com/paper/2512.07990/) (2025) ## Abstract (arXiv) 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.