Related papers: Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learning

Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learning

URL: http://arxiv.org/abs/2105.15064v1
Date: Mon, 31 May 2021 15:51:43 GMT
Title: Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learning
Authors: William Blanzeisky, P\'adraig Cunningham
Abstract summary: We present a multi-objective optimisation strategy that optimises for both balanced accuracy and underestimation. We demonstrate the effectiveness of this strategy on one synthetic and two real-world datasets.
Score: 2.055949720959582
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Algorithmic Bias can be due to bias in the training data or issues with the algorithm itself. These algorithmic issues typically relate to problems with model capacity and regularisation. This underestimation bias may arise because the model has been optimised for good generalisation accuracy without any explicit consideration of bias or fairness. In a sense, we should not be surprised that a model might be biased when it hasn't been "asked" not to be. In this paper, we consider including bias (underestimation) as an additional criterion in model training. We present a multi-objective optimisation strategy using Pareto Simulated Annealing that optimise for both balanced accuracy and underestimation. We demonstrate the effectiveness of this strategy on one synthetic and two real-world datasets.

Related papers

Model Debiasing by Learnable Data Augmentation [19.625915578646758]
This paper proposes a novel 2-stage learning pipeline featuring a data augmentation strategy able to regularize the training. Experiments on synthetic and realistic biased datasets show state-of-the-art classification accuracy, outperforming competing methods.
arXiv Detail & Related papers (2024-08-09T09:19:59Z)
Improving Bias Mitigation through Bias Experts in Natural Language Understanding [10.363406065066538]
We propose a new debiasing framework that introduces binary classifiers between the auxiliary model and the main model. Our proposed strategy improves the bias identification ability of the auxiliary model.
arXiv Detail & Related papers (2023-12-06T16:15:00Z)
Self-supervised debiasing using low rank regularization [59.84695042540525]
Spurious correlations can cause strong biases in deep neural networks, impairing generalization ability. We propose a self-supervised debiasing framework potentially compatible with unlabeled samples. Remarkably, the proposed debiasing framework significantly improves the generalization performance of self-supervised learning baselines.
arXiv Detail & Related papers (2022-10-11T08:26:19Z)
Unsupervised Learning of Unbiased Visual Representations [10.871587311621974]
Deep neural networks are known for their inability to learn robust representations when biases exist in the dataset. We propose a fully unsupervised debiasing framework, consisting of three steps. We employ state-of-the-art supervised debiasing techniques to obtain an unbiased model.
arXiv Detail & Related papers (2022-04-26T10:51:50Z)
General Greedy De-bias Learning [163.65789778416172]
We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
arXiv Detail & Related papers (2021-12-20T14:47:32Z)
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets [74.04426767769785]
We show that specific types of biases may be preferable, depending on the scenario. We design a novel reinforcement learning algorithm, Balanced Q-learning, in which the target is modified to be a convex combination of a pessimistic and an optimistic term.
arXiv Detail & Related papers (2021-11-03T07:30:19Z)
Learning to Estimate Without Bias [57.82628598276623]
Gauss theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints. A second motivation to BCE is in applications where multiple estimates of the same unknown are averaged for improved performance.
arXiv Detail & Related papers (2021-10-24T10:23:51Z)
AutoDebias: Learning to Debias for Recommendation [43.84313723394282]
We propose textitAotoDebias that leverages another (small) set of uniform data to optimize the debiasing parameters. We derive the generalization bound for AutoDebias and prove its ability to acquire the appropriate debiasing strategy.
arXiv Detail & Related papers (2021-05-10T08:03:48Z)
Learning from others' mistakes: Avoiding dataset biases without modeling them [111.17078939377313]
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We show a method for training models that learn to ignore these problematic correlations.
arXiv Detail & Related papers (2020-12-02T16:10:54Z)
Towards Robustifying NLI Models Against Lexical Dataset Biases [94.79704960296108]
This paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features.
arXiv Detail & Related papers (2020-05-10T17:56:10Z)

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