Abstract

Aims: In the realm of Big Data Analytics, ensuring the fairness of data-driven decisionmaking processes is imperative. This abstract introduces the Learning Embedded Fairness Interpretation (LEFI) Model, a novel approach designed to uncover and address data fairness functional requirements with an exceptional accuracy rate of 97%. The model harnesses advanced data mapping and classification analysis techniques, employing Explainable-AI (xAI) for transparent insights into fairness within large datasets.

Methods: The LEFI Model excels in navigating diverse datasets by mapping data elements to discern patterns contributing to biases. Through systematic classification analysis, LEFI identifies potential sources of unfairness, achieving an accuracy rate of 97% in discerning and addressing these issues. This high accuracy empowers data analysts and stakeholders with confidence in the model's assessments, facilitating informed and reliable decision-making. Crucially, the LEFI Model's implementation in Python leverages the power of this versatile programming language. The Python implementation seamlessly integrates advanced mapping, classification analysis, and xAI to provide a robust and efficient solution for achieving data fairness in Big Data Analytics.

Results: This implementation ensures accessibility and ease of adoption for organizations aiming to embed fairness into their data-driven processes. The LEFI Model, with its 97% accuracy, exemplifies a comprehensive solution for data fairness in Big Data Analytics. Moreover, by combining advanced technologies and implementing them in Python, LEFI stands as a reliable framework for organizations committed to ethical data usage.

Conclusion: The model not only contributes to the ongoing dialogue on fairness but also sets a new standard for accuracy and transparency in the analytics pipeline, advocating for a more equitable future in the realm of Big Data Analytics.

Keywords: Big data analytics, entity-relationship mapping, group-based clustering, LEFI, xAI, classification.

Graphical Abstract