Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection

• URL: http://arxiv.org/abs/2204.11164v2
• Date: Fri, 5 May 2023 23:22:32 GMT
• Title: Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection
• Authors: Jiaxin Liu, Yuefei Lyu, Xi Zhang, Sihong Xie
• Abstract summary: This paper addresses the challenges of defining, approximating, and utilizing a new subgroup structure for fair spam detection. We first identify subgroup structures in the review graph that lead to discrepant accuracy in the groups. Comprehensive comparisons against baselines on three large Yelp review datasets demonstrate that the subgroup membership can be identified and exploited for group fairness.
• Score: 13.26226951002133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: User-generated reviews of products are vital assets of online commerce, such as Amazon and Yelp, while fake reviews are prevalent to mislead customers. GNN is the state-of-the-art method that detects suspicious reviewers by exploiting the topologies of the graph connecting reviewers, reviews, and target products. However, the discrepancy in the detection accuracy over different groups of reviewers can degrade reviewer engagement and customer trust in the review websites. Unlike the previous belief that the difference between the groups causes unfairness, we study the subgroup structures within the groups that can also cause discrepancies in treating different groups. This paper addresses the challenges of defining, approximating, and utilizing a new subgroup structure for fair spam detection. We first identify subgroup structures in the review graph that lead to discrepant accuracy in the groups. The complex dependencies over the review graph create difficulties in teasing out subgroups hidden within larger groups. We design a model that can be trained to jointly infer the hidden subgroup memberships and exploits the membership for calibrating the detection accuracy across groups. Comprehensive comparisons against baselines on three large Yelp review datasets demonstrate that the subgroup membership can be identified and exploited for group fairness.

Related papers

• FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups [0.8528401618469597]
  We propose an explainability method tailored to identifying potential bias in subgroups and visualizing the findings in a user friendly manner to end users. We envision FALE to function as an efficient, user friendly, comprehensible and reliable first-stage tool for identifying subgroups with potential bias issues.
  arXiv  Detail & Related papers  (2024-04-29T13:30:57Z)
• A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
  Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups. We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
  arXiv  Detail & Related papers  (2024-01-26T14:21:45Z)
• Fairness Aware Counterfactuals for Subgroups [8.593488857185678]
  We present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We aim to formulate different aspects of the difficulty of individuals in certain subgroups to achieve recourse. We introduce notions of subgroup fairness that are robust, if not totally oblivious, to the cost of achieving recourse.
  arXiv  Detail & Related papers  (2023-06-26T18:03:56Z)
• AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization [109.91265884632239]
  Group distributionally robust optimization (G-DRO) can minimize the worst-case loss over a set of pre-defined groups over training data. We propose AGRO -- Adversarial Group discovery for Distributionally Robust Optimization. AGRO results in 8% higher model performance on average on known worst-groups, compared to prior group discovery approaches.
  arXiv  Detail & Related papers  (2022-12-02T00:57:03Z)
• Outlier-Robust Group Inference via Gradient Space Clustering [50.87474101594732]
  Existing methods can improve the worst-group performance, but they require group annotations, which are often expensive and sometimes infeasible to obtain. We address the problem of learning group annotations in the presence of outliers by clustering the data in the space of gradients of the model parameters. We show that data in the gradient space has a simpler structure while preserving information about minority groups and outliers, making it suitable for standard clustering methods like DBSCAN.
  arXiv  Detail & Related papers  (2022-10-13T06:04:43Z)
• Fair Group-Shared Representations with Normalizing Flows [68.29997072804537]
  We develop a fair representation learning algorithm which is able to map individuals belonging to different groups in a single group. We show experimentally that our methodology is competitive with other fair representation learning algorithms.
  arXiv  Detail & Related papers  (2022-01-17T10:49:49Z)
• Towards Group Robustness in the presence of Partial Group Labels [61.33713547766866]
  spurious correlations between input samples and the target labels wrongly direct the neural network predictions. We propose an algorithm that optimize for the worst-off group assignments from a constraint set. We show improvements in the minority group's performance while preserving overall aggregate accuracy across groups.
  arXiv  Detail & Related papers  (2022-01-10T22:04:48Z)
• Spatio-Temporal Graph Representation Learning for Fraudster Group Detection [50.779498955162644]
  Companies may hire fraudster groups to write fake reviews to either demote competitors or promote their own businesses. To detect such groups, a common model is to represent fraudster groups' static networks. We propose to first capitalize on the effectiveness of the HIN-RNN in both reviewers' representation learning.
  arXiv  Detail & Related papers  (2022-01-07T08:01:38Z)
• HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features [42.30892608083864]
  We propose the first neural approach, HIN-RNN, a Heterogeneous Information Network (HIN) compatible RNN for fraudster group detection. HIN-RNN provides a unifying architecture for learning text representation of each reviewer, with the initial vector as the sum of word embeddings of all review written by the same reviewer. The proposed approach is confirmed to be effective with marked improvement over state-of-the-art approaches on both the Yelp (22% and 12% in terms of recall and F1-value, respectively) and Amazon (4% and 2% in terms of recall and F1-value, respectively
  arXiv  Detail & Related papers  (2021-05-25T01:48:28Z)
• Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews [26.473021051027537]
  In this paper, we collect reviews from the Amazon product review site and manually labelled a set of 923 candidate reviewer groups. The groups are extracted using frequent itemset mining over brand similarities such that users are clustered together if they have mutually reviewed (products of) a lot of brands. We run multiple classifiers for the task of classifying a group based on the reviews written by the users of that group, to determine if the group shows signs of extremity.
  arXiv  Detail & Related papers  (2020-04-13T10:59:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.