Estimating Wage Disparities Using Foundation Models

• URL: http://arxiv.org/abs/2409.09894v2
• Date: Tue, 29 Apr 2025 22:44:29 GMT
• Title: Estimating Wage Disparities Using Foundation Models
• Authors: Keyon Vafa, Susan Athey, David M. Blei,
• Abstract summary: We develop methods for fine-tuning foundation models to perform estimation problems.<n>To demonstrate our ideas, we study gender wage decomposition.<n>We use a custom-built foundation model to decompose the gender wage gap.
• Score: 20.740346109417143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of foundation models marks a paradigm shift in machine learning: instead of training specialized models from scratch, foundation models are first trained on massive datasets before being adapted or fine-tuned to make predictions on smaller datasets. Initially developed for text, foundation models have also excelled at making predictions about social science data. However, while many estimation problems in the social sciences use prediction as an intermediate step, they ultimately require different criteria for success. In this paper, we develop methods for fine-tuning foundation models to perform these estimation problems. We first characterize an omitted variable bias that can arise when a foundation model is only fine-tuned to maximize predictive accuracy. We then provide a novel set of conditions for fine-tuning under which estimates derived from a foundation model are root-n-consistent. Based on this theory, we develop new fine-tuning algorithms that empirically mitigate this omitted variable bias. To demonstrate our ideas, we study gender wage decomposition. This is a statistical estimation problem from econometrics where the goal is to decompose the gender wage gap into components that can and cannot be explained by career histories of workers. Classical methods for decomposing the wage gap employ simple predictive models of wages which condition on coarse summaries of career history that may omit factors that are important for explaining the gap. Instead, we use a custom-built foundation model to decompose the gender wage gap, which captures a richer representation of career history. Using data from the Panel Study of Income Dynamics, we find that career history explains more of the gender wage gap than standard econometric models can measure, and we identify elements of career history that are omitted by standard models but are important for explaining the wage gap.

Related papers

• Does Data-Efficient Generalization Exacerbate Bias in Foundation Models? [2.298227866545911]
  Foundation models have emerged as robust models with label efficiency in diverse domains. It is unclear whether using a large amount of unlabeled data, biased by the presence of sensitive attributes during pre-training, influences the fairness of the model. This research examines the bias in the Foundation model when it is applied to fine-tune the Brazilian Multilabel Ophthalmological dataset.
  arXiv  Detail & Related papers  (2024-08-28T22:14:44Z)
• 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)
• Learning for Counterfactual Fairness from Observational Data [62.43249746968616]
  Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, gender, and age. A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data. In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE.
  arXiv  Detail & Related papers  (2023-07-17T04:08:29Z)
• Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions [50.67412723291881]
  Societal biases present in pre-trained large language models are a critical issue. We propose data intervention strategies as a powerful yet simple technique to reduce gender bias in pre-trained models.
  arXiv  Detail & Related papers  (2023-06-07T16:50:03Z)
• Being Right for Whose Right Reasons? [11.120861224127303]
  This paper presents what we think is a first of its kind, a collection of human rationale annotations augmented with the annotators demographic information. We cover three datasets spanning sentiment analysis and common-sense reasoning, and six demographic groups. We find that models are biased towards aligning best with older and/or white annotators.
  arXiv  Detail & Related papers  (2023-06-01T13:06:43Z)
• Variation of Gender Biases in Visual Recognition Models Before and After Finetuning [29.55318393877906]
  We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. We find that supervised models trained on datasets such as ImageNet-21k are more likely to retain their pretraining biases. We also find that models finetuned on larger scale datasets are more likely to introduce new biased associations.
  arXiv  Detail & Related papers  (2023-03-14T03:42:47Z)
• BLIND: Bias Removal With No Demographics [29.16221451643288]
  We introduce BLIND, a method for bias removal with no prior knowledge of the demographics in the dataset. While training a model on a downstream task, BLIND detects biased samples using an auxiliary model that predicts the main model's success, and down-weights those samples during the training process. Experiments with racial and gender biases in sentiment classification and occupation classification tasks demonstrate that BLIND mitigates social biases without relying on a costly demographic annotation process.
  arXiv  Detail & Related papers  (2022-12-20T18:59:42Z)
• Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases [62.54519787811138]
  We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. We rank images within their classes based on spuriosity, proxied via deep neural features of an interpretable network. Our results suggest that model bias due to spurious feature reliance is influenced far more by what the model is trained on than how it is trained.
  arXiv  Detail & Related papers  (2022-12-05T23:15:43Z)
• Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning [92.89846887298852]
  Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data. Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
  arXiv  Detail & Related papers  (2022-10-11T10:20:31Z)
• The Birth of Bias: A case study on the evolution of gender bias in an English language model [1.6344851071810076]
  We use a relatively small language model, using the LSTM architecture trained on an English Wikipedia corpus. We find that the representation of gender is dynamic and identify different phases during training. We show that gender information is represented increasingly locally in the input embeddings of the model.
  arXiv  Detail & Related papers  (2022-07-21T00:59:04Z)
• 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)
• CAREER: A Foundation Model for Labor Sequence Data [21.38386300423882]
  We develop CAREER, a foundation model for job sequences. CAREER is first fit to large, passively-collected resume data, then fine-tuned to smaller, better-curated datasets for economic inferences. We find that CAREER forms accurate predictions of job sequences, outperforming econometric baselines on three widely-used economics datasets.
  arXiv  Detail & Related papers  (2022-02-16T23:23:50Z)
• A Generative Approach for Mitigating Structural Biases in Natural Language Inference [24.44419010439227]
  In this work, we reformulate the NLI task as a generative task, where a model is conditioned on the biased subset of the input and the label. We show that this approach is highly robust to large amounts of bias. We find that generative models are difficult to train and they generally perform worse than discriminative baselines.
  arXiv  Detail & Related papers  (2021-08-31T17:59:45Z)
• On the Opportunities and Risks of Foundation Models [256.61956234436553]
  We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration.
  arXiv  Detail & Related papers  (2021-08-16T17:50:08Z)
• On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study [65.17429512679695]
  In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Despite ADC's intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models.
  arXiv  Detail & Related papers  (2021-06-02T00:48:33Z)
• 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)

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.