Assessing Fairness in Self-Supervised Learning

Self-Supervised Learning (SSL) is a method of training large models that begins with unsupervised learning before moving to a phase of supervised learning using specific data and labels. This technique has shown good results compared to traditional methods. However, there is little research on how SSL affects Fairness in machine learning models, particularly regarding how well these models perform across different Demographic Groups.

The idea behind this research is to see if models trained with SSL develop less biased Representations of data. This means we want to find out if SSL can help create models that treat everyone equally, regardless of their demographic background. To do this, we designed a framework to assess fairness in SSL, which includes several stages like defining the dataset, pre-training, Fine-tuning, and evaluating how different demographic groups are treated by the model.

#Framework for Assessing Fairness in SSL

We created a five-stage framework to evaluate fairness in SSL. The stages are:

1. Defining Dataset Requirements: The dataset must include at least one protected characteristic, such as age, gender, or race. It should have enough data from various users to allow for fair comparisons. The dataset must also include multiple types (or modalities) of data, such as different sensor readings, and it should be publicly available to ensure transparency.

2. Pre-training: During this stage, a self-supervised learning method is applied to the dataset, allowing the model to learn from data without human labels.

3. Fine-tuning: We use a strategy called gradual unfreezing during this stage. Here, we start by freezing the model's layers and only train a part of it. Later, we gradually unfreeze the layers one by one to fine-tune the model more effectively.

4. Assessing Representation Similarity: We check how similar the model's learned representations are for different demographic groups. This helps us understand if the model treats different groups similarly or differently.

5. Domain-Specific Evaluation Processes: Finally, we measure how well the model performs in practical applications, looking at various metrics to identify biases in predictions across groups.

#Importance of Fairness in Machine Learning

Fairness in machine learning is an important issue. Many real-world applications, especially in sensitive areas such as healthcare, can have serious consequences if models are biased. For example, if a model misclassifies conditions in one demographic group compared to another, it can lead to poor outcomes.

This study focuses on fairness in SSL because SSL is becoming a popular choice for training models. However, it is crucial to ensure that these models do not perpetuate or enhance existing biases in the data.

#Background and Related Work

Existing research has extensively studied the performance of SSL methods, especially in areas like computer vision and natural language processing. However, there has been limited focus on fairness in SSL, particularly in human-centric domains. While there are some examples of SSL being applied in healthcare, the focus has mostly been on performance rather than fairness.

Models trained with SSL often learn from large unlabeled datasets, which can help avoid some of the biases present in labeled data. However, simply using SSL does not guarantee fairness. There are concerns that SSL models might still learn biased representations, particularly if the pre-training data is unbalanced or reflects existing biases.

#Evaluating Fairness

To assess fairness, we look at various metrics that can show how different demographic groups are treated by the model. These metrics help us understand whether the model performs equally well for everyone or if there are discrepancies.

We consider methods to measure group fairness, which looks at the accuracy of predictions for different groups based on sensitive attributes such as gender or race.

#Datasets for Evaluation

We tested our framework on three real-world datasets that contain human-centric data. These datasets include various kinds of information that can be useful for evaluating fairness:

1. MIMIC: This dataset contains medical records and is used to predict in-hospital mortality based on clinical variables like heart rate and oxygen levels.

2. MESA: This dataset consists of sleep data collected from participants to classify sleep-wake states.

3. GLOBEM: This dataset includes behavioral and survey data collected over several years and is used for tasks like depression detection.

Each of these datasets has different levels of representation bias, allowing us to evaluate how our fairness framework performs in diverse scenarios.

#Training and Tuning the Model

For training the SSL model, we built a specific architecture designed to handle time-series data effectively. We used a convolutional neural network (CNN) with multiple layers to extract features from the data.

During fine-tuning, we pay close attention to the setup. We experiment with freezing different layers of the model to see how it impacts performance and fairness. This helps us understand the best way to visualize and interpret the results.

#Results: Performance and Fairness

In our evaluation, we found that self-supervised learning can lead to better fairness while maintaining good performance. The SSL models showed smaller differences in performance between demographic groups compared to traditional supervised models.

#Findings on SSL and Fairness

• SSL models tended to have less bias compared to supervised models, indicating that they could deliver fairer results across various demographic groups.
• For certain fine-tuning strategies, we observed a significant improvement in fairness, with a reduction in the performance gap between the best and worst-performing demographic segments.

#Comparing Performance Across Demographics

When we looked at how models performed across different groups, we discovered notable variations. Certain groups consistently saw lower performance from both SSL and supervised models, illustrating the need for fairness in model design.

Overall, these results support the idea that SSL can enhance fairness in machine learning, especially when models are fine-tuned carefully.

#Conclusion

The findings of this research suggest that self-supervised learning methods have the potential to improve fairness in machine learning applications, particularly in human-centric fields such as healthcare. Our framework for assessing fairness in SSL provides a structured approach to evaluate how well models perform across diverse demographic groups.

While the results are promising, it is crucial to remember that fairness is a complex issue. Models trained on biased data or poor-quality inputs may still produce unfair outcomes. Therefore, further exploration and additional methods are needed to ensure fairness in machine learning models.

The research has implications for how we think about and implement SSL in real-world scenarios. By focusing on fairness as part of the training process, we can work towards developing machine learning systems that are more equitable and beneficial for all users, regardless of their background.

In summary, as SSL continues to gain traction, it is vital to keep fairness in mind, ensuring that these models contribute positively to society by avoiding and mitigating biases that may exist in the data.