AIF360: Implementation Of Bias And Fairness In Machine Learning
AI is rapidly shaping our future, with its ability to perform tasks that normally require human intelligence -such as visual perception, speech recognition, decision-making, and translation between languages. Yet, the significant increase in the reliance on AI starkly impacts the imperative need for fairness, especially in sensitive sectors such as healthcare, criminal justice, and advertising. This article takes an incisive look into a groundbreaking GitHub project known as AIF360, set to determine consistency and eliminate partiality in AI. AIF360, spearheaded by Trusted-AI, adopts a comprehensive approach towards mitigating discrimination in AI applications.
Project Overview:
AIF360 is an open-source library developed to help machine learning models incorporate fairness. Its main objective is to present an all-inclusive set of metrics for datasets and models to test for biases. It is also equipped with algorithms to mitigate bias in datasets and models. AIF360 aims to address the critical need for transparency and accountability in AI and machine learning models and serves primarily developers, data scientists, and researchers involved in AI and machine learning technologies.
Project Features:
Fundamental in its design are metrics to evaluate bias, consisting of 70 bias metrics based on the fairness literature. It also provides 10 mitigation algorithms encompassing pre-processing, in-processing, and post-processing stages of the AI lifecycle. These features help users to identify and mitigate the bias in machine learning models, thus enhancing fairness and preventing discriminatory practices. Its real-life application can be seen in sectors like healthcare where AI is used to predict risk scores. AIF360 ensures that the generated scores are impartial and not prejudiced based on factors such as race, gender, or age.
Technology Stack:
Built using Python, a popular language for machine learning and data science, AIF360 leverages the benefits of Python's simplicity, and extensive library support. AIF360 selects SciKit-Learn, a widely used python library for AI and Machine Learning which helps the project strive towards its goal of bias mitigation. By incorporating these technologies, AIF360 is well equipped to respond to the growing need for fairness in AI models.
Project Structure and Architecture:
The project is built around three main components: fairness metrics, bias mitigation algorithms, and datasets. Fairness metrics are used to measure biases in datasets and machine learning models. Bias mitigation algorithms are designed to reduce bias and achieve fairness in datasets and these models. Datasets component includes various standard datasets modified to align with the fairness context. These modules collectively work to ensure fairness and reduce bias in machine learning models.
