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1. Unfairness Discovery and Prevention For Few-Shot Regression

   https://doi.org/10.1109/ICBK50248.2020.00029  

   Zhao, Chen ; Chen, Feng ( April 2021 , in Proceedings of the IEEE International Conference on Knowledge Graph (ICKG))

   null (Ed.)

   We study fairness in supervised few-shot meta-learning models that are sensitive to discrimination (or bias) in historical data. A machine learning model trained based on biased data tends to make unfair predictions for users from minority groups. Although this problem has been studied before, existing methods mainly aim to detect and control the dependency effect of the protected variables (e.g. race, gender) on target prediction based on a large amount of training data. These approaches carry two major drawbacks that (1) lacking showing a global cause-effect visualization for all variables; (2) lacking generalization of both accuracy and fairness to unseen tasks. In this work, we first discover discrimination from data using a causal Bayesian knowledge graph which not only demonstrates the dependency of the protected variable on target but also indicates causal effects between all variables. Next, we develop a novel algorithm based on risk difference in order to quantify the discriminatory influence for each protected variable in the graph. Furthermore, to protect prediction from unfairness, a the fast-adapted bias-control approach in meta-learning is proposed, which efficiently mitigates statistical disparity for each task and it thus ensures independence of protected attributes on predictions based on biased and few-shot data samples. Distinct from existing meta-learning models, group unfairness of tasks are efficiently reduced by leveraging the mean difference between (un)protected groups for regression problems. Through extensive experiments on both synthetic and real-world data sets, we demonstrate that our proposed unfairness discovery and prevention approaches efficiently detect discrimination and mitigate biases on model output as well as generalize both accuracy and fairness to unseen tasks with a limited amount of training samples. 

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2. A Causal Framework for Discovering and Removing Direct and Indirect Discrimination

   https://doi.org/10.24963/ijcai.2017/549  

   Zhang, Lu ; Wu, Yongkai ; Wu, Xintao ( August 2017 , Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence)

   In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data is used for predictive analysis (e.g., building classifiers). The main drawback of existing methods is that they cannot distinguish the part of influence that is really caused by discrimination from all correlated influences. In our approach, we make use of the causal network to capture the causal structure of the data. Then we model direct and indirect discrimination as the path-specific effects, which accurately identify the two types of discrimination as the causal effects transmitted along different paths in the network. Based on that, we propose an effective algorithm for discovering direct and indirect discrimination, as well as an algorithm for precisely removing both types of discrimination while retaining good data utility. Experiments using the real dataset show the effectiveness of our approaches.

    

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3. Fairness for robust learning to rank

   Memarrast, Omid ; Rezaei, Ashkan ; Fathony, Rizal ; Ziebart, Brian ( May 2023 , Pacific-Asia Conference on Knowledge Discovery and Data Mining)

   While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure based on different protected attributes such as gender or race. To achieve this type of group fairness for ranking, we derive a new ranking system from the first principles of distributional robustness. We formulate a minimax game between a player choosing a distribution over rankings to maximize utility while satisfying fairness constraints against an adversary seeking to minimize utility while matching statistics of the training data. Rather than maximizing utility and fairness for the specific training data, this approach efficiently produces robust utility and fairness for a much broader family of distributions of rankings that include the training data. We show that our approach provides better utility for highly fair rankings than existing baseline methods. 

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4. Fairness for robust learning to rank

   Memarrast, Omid ; Rezaei, Ashkan ; Ziebart, Brian D. ( May 2023 , PAKDD 2023: Advances in Knowledge Discovery and Data Mining)

   While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure based on different protected attributes such as gender or race. To achieve this type of group fairness for ranking, we derive a new ranking system from the first principles of distributional robustness. We formulate a minimax game between a player choosing a distribution over rankings to maximize utility while satisfying fairness constraints against an adversary seeking to minimize utility while matching statistics of the training data. Rather than maximizing utility and fairness for the specific training data, this approach efficiently produces robust utility and fairness for a much broader family of distributions of rankings that include the training data. We show that our approach provides better utility for highly fair rankings than existing baseline methods. 

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5. Towards Optimizing Ranking in Grid-Layout for Provider-side Fairness

   Raj, Amifa ; Ekstrand, Michael D. ( March 2024 , ECIR 2024: Advances in Information Retrieval)

   Information access systems, such as search engines and recommender systems, order and position results based on their estimated relevance. These results are then evaluated for a range of concerns, including provider-side fairness: whether exposure to users is fairly distributed among items and the people who created them. Several fairness-aware ranking and re-ranking techniques have been proposed to ensure fair exposure for providers, but this work focuses almost exclusively on linear layouts in which items are displayed in single ranked list. Many widely-used systems use other layouts, such as the grid views common in streaming platforms, image search, and other applications. Providing fair exposure to providers in such layouts is not well-studied. We seek to fill this gap by providing a grid-aware re-ranking algorithm to optimize layouts for provider-side fairness by adapting existing re-ranking techniques to grid-aware browsing models, and an analysis of the effect of grid-specific factors such as device size on the resulting fairness optimization. Our work provides a starting point and identifies open gaps in ensuring provider-side fairness in grid-based layouts. 

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