Falcon: Fair Active Learning using Multi-armed Bandits

Biased data can lead to unfair machine learning models, highlighting the importance of embedding fairness at the beginning of data analysis, particularly during dataset curation and labeling. In response, we propose Falcon, a scalable fair active learning framework. Falcon adopts a data-centric approach that improves machine learning model fairness via strategic sample selection. Given a user-specified group fairness measure, Falcon identifies samples from "target groups" (e.g., (attribute=female, label=positive)) that are the most informative for improving fairness. However, a challenge arises since these target groups are defined using ground truth labels that are not available during sample selection. To handle this, we propose a novel trial-and-error method, where we postpone using a sample if the predicted label is different from the expected one and falls outside the target group. We also observe the trade-off that selecting more informative samples results in higher likelihood of postponing due to undesired label prediction, and the optimal balance varies per dataset. We capture the trade-off between informativeness and postpone rate as policies and propose to automatically select the best policy using adversarial multi-armed bandit methods, given their computational efficiency and theoretical guarantees. Experiments show that Falcon significantly outperforms existing fair active learning approaches in terms of fairness and accuracy and is more efficient. In particular, only Falcon supports a proper trade-off between accuracy and fairness where its maximum fairness score is 1.8-4.5x higher than the second-best results.Comment: Accepted to VLDB 202

Fair Robust Active Learning by Joint Inconsistency

Fair Active Learning (FAL) utilized active learning techniques to achieve high model performance with limited data and to reach fairness between sensitive groups (e.g., genders). However, the impact of the adversarial attack, which is vital for various safety-critical machine learning applications, is not yet addressed in FAL. Observing this, we introduce a novel task, Fair Robust Active Learning (FRAL), integrating conventional FAL and adversarial robustness. FRAL requires ML models to leverage active learning techniques to jointly achieve equalized performance on benign data and equalized robustness against adversarial attacks between groups. In this new task, previous FAL methods generally face the problem of unbearable computational burden and ineffectiveness. Therefore, we develop a simple yet effective FRAL strategy by Joint INconsistency (JIN). To efficiently find samples that can boost the performance and robustness of disadvantaged groups for labeling, our method exploits the prediction inconsistency between benign and adversarial samples as well as between standard and robust models. Extensive experiments under diverse datasets and sensitive groups demonstrate that our method not only achieves fairer performance on benign samples but also obtains fairer robustness under white-box PGD attacks compared with existing active learning and FAL baselines. We are optimistic that FRAL would pave a new path for developing safe and robust ML research and applications such as facial attribute recognition in biometrics systems.Comment: 11 pages, 3 figure

More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias

An increased awareness concerning risks of algorithmic bias has driven a surge of efforts around bias mitigation strategies. A vast majority of the proposed approaches fall under one of two categories: (1) imposing algorithmic fairness constraints on predictive models, and (2) collecting additional training samples. Most recently and at the intersection of these two categories, methods that propose active learning under fairness constraints have been developed. However, proposed bias mitigation strategies typically overlook the bias presented in the observed labels. In this work, we study fairness considerations of active data collection strategies in the presence of label bias. We first present an overview of different types of label bias in the context of supervised learning systems. We then empirically show that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem. Our results illustrate the unintended consequences of deploying a model that attempts to mitigate a single type of bias while neglecting others, emphasizing the importance of explicitly differentiating between the types of bias that fairness-aware algorithms aim to address, and highlighting the risks of neglecting label bias during data collection

Exploring the Impact of Lay User Feedback for Improving AI Fairness

Fairness in AI is a growing concern for high-stakes decision making. Engaging stakeholders, especially lay users, in fair AI development is promising yet overlooked. Recent efforts explore enabling lay users to provide AI fairness-related feedback, but there is still a lack of understanding of how to integrate users' feedback into an AI model and the impacts of doing so. To bridge this gap, we collected feedback from 58 lay users on the fairness of a XGBoost model trained on the Home Credit dataset, and conducted offline experiments to investigate the effects of retraining models on accuracy, and individual and group fairness. Our work contributes baseline results of integrating user fairness feedback in XGBoost, and a dataset and code framework to bootstrap research in engaging stakeholders in AI fairness. Our discussion highlights the challenges of employing user feedback in AI fairness and points the way to a future application area of interactive machine learning

Active Sampling for Min-Max Fairness

We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization. The key intuition behind our approach is to use at each timestep a datapoint from the group that is worst off under the current model for updating the model. The ease of implementation and the generality of our robust formulation make it an attractive option for improving model performance on disadvantaged groups. For convex learning problems, such as linear or logistic regression, we provide a fine-grained analysis, proving the rate of convergence to a min-max fair solution

Look and You Will Find It:Fairness-Aware Data Collection through Active Learning

Machine learning models are often trained on data sets subject to selection bias. In particular, selection bias can be hard to avoid in scenarios where the proportion of positives is low and labeling is expensive, such as fraud detection. However, when selection bias is related to sensitive characteristics such as gender and race, it can result in an unequal distribution of burdens across sensitive groups, where marginalized groups are misrepresented and disproportionately scrutinized. Moreover, when the predictions of existing systems affect the selection of new labels, a feedback loop can occur in which selection bias is amplified over time. In this work, we explore the effectiveness of active learning approaches to mitigate fairnessrelated harm caused by selection bias. Active learning approaches aim to select the most informative instances from unlabeled data. We hypothesize that this characteristic steers data collection towards underexplored areas of the feature space and away from overexplored areas – including areas affectedby selection bias. Our preliminary simulation results confirm the intuition that active learning can mitigate the negative consequences of selection bias, compared to both the baseline scenario and random sampling.<br/

Benchmarking Multi-Domain Active Learning on Image Classification

Active learning aims to enhance model performance by strategically labeling informative data points. While extensively studied, its effectiveness on large-scale, real-world datasets remains underexplored. Existing research primarily focuses on single-source data, ignoring the multi-domain nature of real-world data. We introduce a multi-domain active learning benchmark to bridge this gap. Our benchmark demonstrates that traditional single-domain active learning strategies are often less effective than random selection in multi-domain scenarios. We also introduce CLIP-GeoYFCC, a novel large-scale image dataset built around geographical domains, in contrast to existing genre-based domain datasets. Analysis on our benchmark shows that all multi-domain strategies exhibit significant tradeoffs, with no strategy outperforming across all datasets or all metrics, emphasizing the need for future research

Adaptive Boosting with Fairness-aware Reweighting Technique for Fair Classification

Machine learning methods based on AdaBoost have been widely applied to various classification problems across many mission-critical applications including healthcare, law and finance. However, there is a growing concern about the unfairness and discrimination of data-driven classification models, which is inevitable for classical algorithms including AdaBoost. In order to achieve fair classification, a novel fair AdaBoost (FAB) approach is proposed that is an interpretable fairness-improving variant of AdaBoost. We mainly investigate binary classification problems and focus on the fairness of three different indicators (i.e., accuracy, false positive rate and false negative rate). By utilizing a fairness-aware reweighting technique for base classifiers, the proposed FAB approach can achieve fair classification while maintaining the advantage of AdaBoost with negligible sacrifice of predictive performance. In addition, a hyperparameter is introduced in FAB to show preferences for the fairness-accuracy trade-off. An upper bound for the target loss function that quantifies error rate and unfairness is theoretically derived for FAB, which provides a strict theoretical support for the fairness-improving methods designed for AdaBoost. The effectiveness of the proposed method is demonstrated on three real-world datasets (i.e., Adult, COMPAS and HSLS) with respect to the three fairness indicators. The results are accordant with theoretic analyses, and show that (i) FAB significantly improves classification fairness at a small cost of accuracy compared with AdaBoost; and (ii) FAB outperforms state-of-the-art fair classification methods including equalized odds method, exponentiated gradient method, and disparate mistreatment method in terms of the fairness-accuracy trade-off

Mitigating Algorithmic Bias with Limited Annotations

Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information. When sensitive attributes are not disclosed or available, it is needed to manually annotate a small part of the training data to mitigate bias. However, the skewed distribution across different sensitive groups preserves the skewness of the original dataset in the annotated subset, which leads to non-optimal bias mitigation. To tackle this challenge, we propose Active Penalization Of Discrimination (APOD), an interactive framework to guide the limited annotations towards maximally eliminating the effect of algorithmic bias. The proposed APOD integrates discrimination penalization with active instance selection to efficiently utilize the limited annotation budget, and it is theoretically proved to be capable of bounding the algorithmic bias. According to the evaluation on five benchmark datasets, APOD outperforms the state-of-the-arts baseline methods under the limited annotation budget, and shows comparable performance to fully annotated bias mitigation, which demonstrates that APOD could benefit real-world applications when sensitive information is limited