De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data

• URL: http://arxiv.org/abs/2405.00910v1
• Date: Wed, 1 May 2024 23:46:44 GMT
• Title: De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data
• Authors: Nicholas Tenev,
• Abstract summary: This paper adds counterfactual (simulated) ethnic bias to real data on mortgage application decisions. It shows that this bias is replicated by a machine learning model (XGBoost) even when ethnicity is not used as a predictive variable.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prediction models can improve efficiency by automating decisions such as the approval of loan applications. However, they may inherit bias against protected groups from the data they are trained on. This paper adds counterfactual (simulated) ethnic bias to real data on mortgage application decisions, and shows that this bias is replicated by a machine learning model (XGBoost) even when ethnicity is not used as a predictive variable. Next, several other de-biasing methods are compared: averaging over prohibited variables, taking the most favorable prediction over prohibited variables (a novel method), and jointly minimizing errors as well as the association between predictions and prohibited variables. De-biasing can recover some of the original decisions, but the results are sensitive to whether the bias is effected through a proxy.

Related papers

• Editable Fairness: Fine-Grained Bias Mitigation in Language Models [52.66450426729818]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases. FAST surpasses state-of-the-art baselines with superior debiasing performance. This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Inference-Time Selective Debiasing [27.578390085427156]
  We propose selective debiasing -- an inference-time safety mechanism that aims to increase the overall quality of models. We identify the potentially biased model predictions and, instead of discarding them, we debias them using LEACE -- a post-processing debiasing method. Experiments with text classification datasets demonstrate that selective debiasing helps to close the performance gap between post-processing methods and at-training and pre-processing debiasing techniques.
  arXiv  Detail & Related papers  (2024-07-27T21:56:23Z)
• Fast Model Debias with Machine Unlearning [54.32026474971696]
  Deep neural networks might behave in a biased manner in many real-world scenarios. Existing debiasing methods suffer from high costs in bias labeling or model re-training. We propose a fast model debiasing framework (FMD) which offers an efficient approach to identify, evaluate and remove biases.
  arXiv  Detail & Related papers  (2023-10-19T08:10:57Z)
• Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations [4.960902915238239]
  We propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing.
  arXiv  Detail & Related papers  (2023-04-17T08:56:55Z)
• 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)
• D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias [57.87117733071416]
  We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases. A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
  arXiv  Detail & Related papers  (2022-08-10T03:41:48Z)
• Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction [4.874780144224057]
  A biased model can make decisions that disproportionately harm certain groups in society. Much work has been devoted to measuring unfairness in static ML environments, but not in dynamic, performative prediction ones. We propose a taxonomy to characterize bias in the data, and study cases where it is shaped by model behaviour.
  arXiv  Detail & Related papers  (2022-06-27T10:56:04Z)
• xFAIR: Better Fairness via Model-based Rebalancing of Protected Attributes [15.525314212209564]
  Machine learning software can generate models that inappropriately discriminate against specific protected social groups. We propose xFAIR, a model-based extrapolation method, that is capable of both mitigating bias and explaining the cause.
  arXiv  Detail & Related papers  (2021-10-03T22:10:14Z)
• Balancing out Bias: Achieving Fairness Through Training Reweighting [58.201275105195485]
  Bias in natural language processing arises from models learning characteristics of the author such as gender and race. Existing methods for mitigating and measuring bias do not directly account for correlations between author demographics and linguistic variables. This paper introduces a very simple but highly effective method for countering bias using instance reweighting.
  arXiv  Detail & Related papers  (2021-09-16T23:40:28Z)
• 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)
• Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
  We show that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
  arXiv  Detail & Related papers  (2020-02-17T19:23:36Z)

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.