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Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer Machine Learning


Core Concepts
Diverse Class-Aware Self-Training (DCAST) is a model-agnostic semi-supervised learning framework that leverages unlabeled data to mitigate selection bias and improve the fairness of machine learning models.
Abstract

The article introduces two key contributions to address selection bias in machine learning:

  1. Hierarchy bias: A bias induction technique that generates complex multivariate and class-specific selection bias in the training data. Hierarchy bias uses clustering to identify distinct groups of samples per class and then skews the representation of these groups during sample selection.

  2. Diverse Class-Aware Self-Training (DCAST): A semi-supervised learning framework that gradually incorporates unlabeled data in a class-aware manner, guided by two active bias mitigation strategies:

    • Class-Aware Self-Training (CAST): Performs pseudo-labeling and sample selection separately per class to address class-specific bias.
    • Diverse CAST (DCAST): Further promotes sample diversity in the pseudo-labeled set to counter confidence-induced bias.

The authors evaluate hierarchy bias induction and (D)CAST bias mitigation across 11 datasets, comparing them against conventional self-training and six prominent domain adaptation techniques. They find that hierarchy bias induces the most challenging type of selection bias, leading to a significant decrease in prediction performance of supervised models. In contrast, (D)CAST strategies, especially with higher diversity, are able to effectively mitigate this bias and outperform the competing approaches, particularly when paired with neural network models.

The key insights are:

  • Hierarchy bias effectively induces complex multivariate and class-specific selection bias in the data.
  • Class-awareness and diversity in (D)CAST improve robustness to selection bias compared to conventional self-training and domain adaptation methods.
  • (D)CAST is a promising model-agnostic strategy to achieve fairer learning beyond identifiable bias.
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Stats
"Hierarchy bias generates a biased selection of samples from a given dataset in a class-aware and multivariate manner." "Hierarchy bias induction takes as input a data matrix X, a label matrix Y, a parameter k denoting the number of samples to select per class, and a bias parameter b ∈[0, 1] denoting the ratio of samples that should be selected from the identified mixture." "At each iteration, (D)CAST selects a subset of s pseudo-labeled samples (sc per class) to incorporate into the labeled set for the next iteration." "For DCAST, the set of s × d candidate samples is reduced to s diverse samples by identifying s clusters and selecting the most confidently predicted sample from each cluster."
Quotes
"Fairness in machine learning seeks to mitigate model bias against individuals based on sensitive features such as sex or age, often caused by an uneven representation of the population in the training data due to selection bias." "Unknown biases are often present when data is complex and high-dimensional, data collection is non-random, and knowledge of the domain is incomplete." "We argue that unfairness mitigation should thus address bias more generally, beyond what can be ascribed to sensitive features."

Deeper Inquiries

How can the hierarchy bias induction technique be extended to handle more complex data structures, such as graph-structured or time-series data?

The hierarchy bias induction technique, as described in the context, primarily relies on agglomerative hierarchical clustering to identify distinct groups of samples based on their feature representations. To extend this technique for more complex data structures like graph-structured or time-series data, several adaptations can be made: Graph-Structured Data: For graph data, traditional distance metrics may not be applicable. Instead, one could utilize graph-based clustering algorithms such as spectral clustering or community detection methods (e.g., Louvain method) that consider the connectivity and relationships between nodes. The bias induction process could then focus on selecting samples from densely connected subgraphs, thereby ensuring that the induced bias reflects the underlying graph structure. Additionally, incorporating node embeddings (e.g., using Graph Neural Networks) can help in capturing the relationships and features of nodes more effectively, allowing for a more nuanced bias induction. Time-Series Data: Time-series data presents unique challenges due to its sequential nature. To adapt hierarchy bias induction, one could employ time-series clustering techniques such as Dynamic Time Warping (DTW) or shape-based clustering methods that account for temporal patterns. The bias induction could then be performed by identifying clusters of similar time-series segments and skewing the selection towards specific temporal patterns or events. Moreover, incorporating temporal features (e.g., seasonality, trends) into the clustering process can enhance the ability to induce meaningful biases that reflect the dynamics of the data. Multimodal Data: In cases where data is multimodal (e.g., combining images, text, and structured data), a hierarchical bias induction approach could leverage multi-view clustering techniques that consider the relationships across different modalities. This would involve creating a unified representation that captures the interactions between modalities, allowing for a more comprehensive bias induction process. By integrating these advanced clustering techniques and representations tailored to the specific characteristics of graph-structured and time-series data, the hierarchy bias induction method can be effectively extended to handle a broader range of complex data structures.

What are the potential limitations of the (D)CAST framework, and how could it be further improved to handle a wider range of bias types and data modalities?

The (D)CAST framework presents a promising approach to mitigating selection bias in machine learning, but it also has several potential limitations: Dependence on Quality of Unlabeled Data: The effectiveness of (D)CAST heavily relies on the availability and quality of unlabeled data. If the unlabeled data is not representative of the underlying population or contains biases itself, the model may inadvertently learn and propagate these biases. To improve this, techniques such as active learning could be integrated, where the model selectively queries the most informative samples from the unlabeled pool to enhance the quality of the training data. Scalability Issues: As the size of the dataset increases, the computational complexity of clustering and distance calculations in the diversity module may become prohibitive. To address this, approximate nearest neighbor search algorithms or dimensionality reduction techniques (e.g., PCA, t-SNE) could be employed to speed up the clustering process while maintaining the integrity of the sample diversity. Limited Bias Types Addressed: While (D)CAST focuses on selection bias, it may not adequately address other forms of bias, such as label bias or measurement bias. Future iterations of the framework could incorporate mechanisms to identify and mitigate these additional bias types, perhaps through the integration of adversarial training techniques that explicitly target different bias sources. Data Modalities: The current implementation of (D)CAST is primarily designed for tabular data. To handle a wider range of data modalities, such as images or text, the framework could be adapted to include specialized embedding techniques (e.g., CNNs for images, transformers for text) that capture the unique characteristics of these data types. This would allow (D)CAST to leverage the rich information contained in various modalities while maintaining its core principles of class-awareness and diversity. By addressing these limitations through enhancements in data quality management, computational efficiency, bias type coverage, and modality adaptability, the (D)CAST framework could be significantly improved to handle a broader spectrum of bias types and data structures.

What are the implications of the findings in this work for the broader challenge of achieving fairness in machine learning systems deployed in real-world applications?

The findings from the DCAST framework and hierarchy bias induction technique have significant implications for the broader challenge of achieving fairness in machine learning systems: Comprehensive Bias Mitigation: The introduction of methods that address not only selection bias but also the complexities of multivariate and class-specific biases highlights the need for a more holistic approach to fairness in machine learning. This work emphasizes that fairness cannot be achieved by merely focusing on identifiable biases linked to sensitive features; rather, it requires a thorough understanding and mitigation of all potential biases present in the data. Model-Agnostic Solutions: The model-agnostic nature of (D)CAST allows for its application across various machine learning architectures, making it a versatile tool for practitioners. This flexibility is crucial in real-world applications where different models may be employed for different tasks. The ability to adapt bias mitigation strategies to various contexts enhances the potential for fairer outcomes across diverse applications. Evaluation of Model Robustness: The proposed hierarchy bias induction technique provides a framework for evaluating model robustness against complex bias scenarios. This is particularly important in real-world applications where data distributions can shift over time. By establishing methods to induce and evaluate bias, practitioners can better understand the limitations of their models and take proactive steps to ensure fairness. Guidance for Future Research: The findings serve as a foundation for future research in fairness and bias mitigation. They encourage the exploration of additional bias types, the development of more sophisticated evaluation metrics, and the integration of fairness considerations into the entire machine learning pipeline—from data collection to model deployment. Ethical Considerations: Ultimately, the work underscores the ethical responsibility of machine learning practitioners to ensure that their models do not perpetuate or exacerbate existing inequalities. By providing tools and methodologies for bias mitigation, this research contributes to the ongoing discourse on ethical AI and the need for fairness in automated decision-making systems. In summary, the implications of this work extend beyond technical advancements; they advocate for a more equitable approach to machine learning that prioritizes fairness and accountability in real-world applications.
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