Practical Aspects of Fairness In AI

Published:
Python Machine Learning Statistics

Awards & Recognition

Introduction

As Artificial Intelligence (AI) becomes more embedded in society, ensuring fairness in its decision-making processes is crucial. AI systems are increasingly used to make sensitive predictions, such as determining creditworthiness, hiring suitability, and the likelihood of criminal re-offense. While these models promise efficiency and uniformity in decision-making, they can unintentionally perpetuate and even amplify existing biases.

In my Master’s project at Imperial College London, I aimed to address these concerns by extending IBM’s open-source AI Fairness 360 (AIF360) toolkit. The goal was to integrate three state-of-the-art fairness remediation algorithms: Subgroup Fairness, Instantaneous Fairness, and Distributional Repair. These methods were adapted to be versatile, easy to implement, and scalable across a variety of datasets and fairness use cases.

Understanding Fairness in AI

Fairness in AI refers to designing systems that ensure equitable treatment regardless of an individual’s sensitive attributes, such as race, gender, or age. There are several ways to define fairness, each with its advantages and limitations.

Some common fairness notions include:

Project Goals

The main objectives of this project were to:

Implemented Algorithms

1. Subgroup and Instantaneous Fairness

The Subgroup Fairness and Instantaneous Fairness algorithms focus on minimizing disparities between different groups over time and across subgroups. These algorithms expand on a conventional fairness model to address scenarios where fairness might vary over different time periods or among subgroups with multiple sensitive attributes.

Subgroup Fairness ensures equitable treatment by minimizing the maximum average loss across all subgroups over an entire time period. For example, if an AI model is used in hiring, Subgroup Fairness aims to balance the hiring success rates across all subgroups (e.g., gender and race combinations) over an extended period.

Instantaneous Fairness, on the other hand, aims to maintain fairness at each individual time point by equalizing the loss for all subgroups at each time step. This algorithm is particularly relevant for applications requiring real-time fairness, such as news feed algorithms, where it is crucial that all subgroups have an equal chance of seeing relevant news stories.

Both algorithms employ min-max optimization, which aims to balance the losses across subgroups, ensuring that no subgroup is disproportionately disadvantaged. By addressing both long-term and real-time fairness, these algorithms provide a robust framework for fairness in AI systems.

2. Distributional Repair Algorithm

The Distributional Repair algorithm takes a different approach by focusing on conditional independence. The idea is to reduce the correlation between sensitive and non-sensitive features in a dataset, thus increasing fairness before the model is even trained.

This algorithm employs Optimal Transport (OT) theory to modify the distribution of dataset features. It essentially “repairs” the dataset by reducing dependencies between sensitive features (e.g., race or gender) and the model’s input features. This method enhances fairness at the preprocessing stage, making it a powerful technique to integrate with models that act as “black boxes.”

Optimal Transport identifies an optimal way to shift data points to achieve a target distribution, reducing potential bias while preserving the original structure of the data.

Methodology and Implementation

Algorithm Development

The project involved generalizing and abstracting research-specific code to work seamlessly within the AIF360 toolkit. This required redesigning the algorithms to follow standard practices for open-source tools, including:

Evaluation Metrics

To evaluate the effectiveness of these algorithms, the project focused on the following metrics:

Evaluation and Results

The implemented algorithms were evaluated on multiple datasets, including the COMPAS dataset (criminal re-offense predictions) and the Adult dataset (income predictions). Key findings include:

Key Findings

Future Scope

While the algorithms performed well, there are areas for further improvement:

Conclusion

Through this project, I successfully integrated state-of-the-art fairness algorithms into the AIF360 toolkit. By adapting these research algorithms into accessible, generalized tools, the project contributes to the ongoing effort to make AI systems more fair and transparent.

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