Fairness-Aware Streaming Feature Selection with Causal Graphs

• URL: http://arxiv.org/abs/2408.12665v1
• Date: Sat, 17 Aug 2024 00:41:02 GMT
• Title: Fairness-Aware Streaming Feature Selection with Causal Graphs
• Authors: Leizhen Zhang, Lusi Li, Di Wu, Sheng Chen, Yi He,
• Abstract summary: Streaming Feature Selection with Causal Fairness builds causal graphs egocentric to prediction label and protected feature. We benchmark SFCF on five datasets widely used in streaming feature research.
• Score: 10.644488289941021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Its crux lies in the optimization of a tradeoff between accuracy and fairness of resultant models on the selected feature subset. The technical challenge of our setting is twofold: 1) streaming feature inputs, such that an informative feature may become obsolete or redundant for prediction if its information has been covered by other similar features that arrived prior to it, and 2) non-associational feature correlation, such that bias may be leaked from those seemingly admissible, non-protected features. To overcome this, we propose Streaming Feature Selection with Causal Fairness (SFCF) that builds two causal graphs egocentric to prediction label and protected feature, respectively, striving to model the complex correlation structure among streaming features, labels, and protected information. As such, bias can be eradicated from predictive modeling by removing those features being causally correlated with the protected feature yet independent to the labels. We theorize that the originally redundant features for prediction can later become admissible, when the learning accuracy is compromised by the large number of removed features (non-protected but can be used to reconstruct bias information). We benchmark SFCF\ on five datasets widely used in streaming feature research, and the results substantiate its performance superiority over six rival models in terms of efficiency and sparsity of feature selection and equalized odds of the resultant predictive models.

Related papers

• Towards Robust Text Classification: Mitigating Spurious Correlations with Causal Learning [2.7813683000222653]
  We propose the Causally Calibrated Robust ( CCR) to reduce models' reliance on spurious correlations. CCR integrates a causal feature selection method based on counterfactual reasoning, along with an inverse propensity weighting (IPW) loss function. We show that CCR state-of-the-art performance among methods without group labels, and in some cases, it can compete with the models that utilize group labels.
  arXiv  Detail & Related papers  (2024-11-01T21:29:07Z)
• Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation Model [86.9619638550683]
  Vision-language foundation models have exhibited remarkable success across a multitude of downstream tasks due to their scalability on extensive image-text paired data. However, these models display significant limitations when applied to downstream tasks, such as fine-grained image classification, as a result of decision shortcuts''
  arXiv  Detail & Related papers  (2024-03-01T09:01:53Z)
• On the Foundations of Shortcut Learning [20.53986437152018]
  We study how predictivity and availability interact to shape models' feature use. We find that linear models are relatively unbiased, but introducing a single hidden layer with ReLU or Tanh units yields a bias.
  arXiv  Detail & Related papers  (2023-10-24T22:54:05Z)
• Causal Feature Selection via Transfer Entropy [59.999594949050596]
  Causal discovery aims to identify causal relationships between features with observational data. We introduce a new causal feature selection approach that relies on the forward and backward feature selection procedures. We provide theoretical guarantees on the regression and classification errors for both the exact and the finite-sample cases.
  arXiv  Detail & Related papers  (2023-10-17T08:04:45Z)
• Stubborn Lexical Bias in Data and Models [50.79738900885665]
  We use a new statistical method to examine whether spurious patterns in data appear in models trained on the data. We apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations. Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models.
  arXiv  Detail & Related papers  (2023-06-03T20:12:27Z)
• On the utility of feature selection in building two-tier decision trees [0.0]
  It is demonstrated that the synergistic effect of complementary features mutually amplifying each other in the construction of two-tier decision trees can be interfered with by another feature. Removing or eliminating the interfering feature can improve performance by up to 24 times. It is concluded that this broadens the scope of feature selection methods for cases where data and computational resources are sufficient.
  arXiv  Detail & Related papers  (2022-12-29T20:10:45Z)
• Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data [1.76179873429447]
  We propose a data preprocessing technique that can detect instances ascribing a specific kind of bias that should be removed from the dataset before training. In particular, we claim that in the problem settings where instances exist with similar feature but different labels caused by variation in protected attributes, an inherent bias gets induced in the dataset.
  arXiv  Detail & Related papers  (2022-10-24T13:04:07Z)
• Understanding Interlocking Dynamics of Cooperative Rationalization [90.6863969334526]
  Selective rationalization explains the prediction of complex neural networks by finding a small subset of the input that is sufficient to predict the neural model output. We reveal a major problem with such cooperative rationalization paradigm -- model interlocking. We propose a new rationalization framework, called A2R, which introduces a third component into the architecture, a predictor driven by soft attention as opposed to selection.
  arXiv  Detail & Related papers  (2021-10-26T17:39:18Z)
• Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately [83.68135652247496]
  A natural remedy is to remove spurious features from the model. We show that removal of spurious features can decrease accuracy due to inductive biases. We also show that robust self-training can remove spurious features without affecting the overall accuracy.
  arXiv  Detail & Related papers  (2020-12-07T23:08:59Z)
• Learning Causal Models Online [103.87959747047158]
  Predictive models can rely on spurious correlations in the data for making predictions. One solution for achieving strong generalization is to incorporate causal structures in the models. We propose an online algorithm that continually detects and removes spurious features.
  arXiv  Detail & Related papers  (2020-06-12T20:49:20Z)

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.