FairTrain

Inspiration

In an era where economic instability affects millions, AI is often seen as a magic solution. But unchecked, AI can perpetuate biases, reinforcing existing inequalities. FairTrain was inspired by the need to ensure that AI’s power is used to uplift everyone, not just a select few. We’re here to make fairness a priority in machine learning and make decisions that benefit all.

What it does

FairTrain demonstrates that traditional machine learning methods and metrics can overlook important biases, especially in sensitive areas like credit scoring. By reevaluating how these models are trained, it ensures more balanced and reliable decision-making in high-stakes applications.

How we built it

I started by preprocessing the datasets, including handling missing values and normalizing features. Then, I performed feature engineering, creating meaningful variables and applying one-hot encoding for categorical data. After that, I implemented three traditional models: Logistic Regression, Random Forest, and XGBoost, fine-tuning them with bias and variance metrics. Finally, I used FairLearn to integrate fairness metrics like Demographic Parity, ensuring more balanced predictions, particularly for sensitive applications like credit scoring.

Challenges we ran into

The dataset was highly unbalanced, with credit card approvals being much more common than declines. This created challenges in model performance, especially when dealing with overfitting. The majority class was also problematic, leading to skewed results. Setting up fairness, particularly to avoid bias based on demographic factors, was another challenge. Additionally, XGBoost proved to be a very picky model, requiring precise data inputs in the correct order and perfect alignment of features, making it quite challenging to work with.

Accomplishments that we're proud of

The final model achieved impressive results using traditional metrics, particularly in handling the unbalanced dataset with a dominant majority class. It successfully eliminated demographic-based biases, proving its superiority for this task. Additionally, the model showed significant improvements in fairness, demonstrating its capability to make more equitable decisions without compromising performance.

What we learned

Machine learning is an actively growing field with applications that are expanding rapidly and, in some cases, uncontrollably. While it holds immense potential, it’s crucial to understand both its advantages and disadvantages, especially when vulnerable people may suffer silently due to biased or unfair algorithms. The responsibility lies in ensuring that these systems are fair, transparent, and do not perpetuate inequalities.

What's next for FairTrain

FairTrain will evolve to integrate advanced fairness algorithms and expand to new AI applications. Future updates will focus on real-time fairness monitoring, diverse datasets, and continuous refinement, ensuring that ethical AI deployment remains at the forefront of machine learning advancements.

Built With

• chart.js
• fairlearn
• flask
• numpy
• pandas
• python
• react
• scikit-learn
• xgboost