Bipol: A novel multi-axes bias evaluation metric with explainability for NLP

Alkhaled, Lama; Adewumi, Tosin; Sabry, Sana Sabah

doi:10.1016/j.nlp.2023.100030

Cited by 2 publications

(1 citation statement)

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“…In today's world, machine learning (ML) applications have become integral to various aspects of our lives (Kim et al, 2018). Whether it is transportation, healthcare, document analysis, or any other industry (Adewumi et al, 2022;Alkhaled et al, 2023;Kanchi et al, 2022;Khan et al, 2022), the proliferation of ML applications has been rapid (Amershi et al, 2019). ML is a fundamental component of artificial intelligence (AI), where data and algorithms are utilized to enable AI systems to mimic human behavior and cognition.…”

Section: Introductionmentioning

confidence: 99%

Supportive Environment for Better Data Management Stage in the Cycle of ML Process

Alkhaled,

Khamis

2023

AIA

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The objective of this study is to explore the process of developing Artificial Intelligence (AI) and machine learning (ML) applications to establish an optimal support environment. The primary stages of ML include problem understanding, data management, model building, model deployment, and maintenance. This paper specifically focuses on examining the data management stage of ML development and the challenges it presents, as it is crucial for achieving accurate end models. During this stage, the major obstacle encountered was the scarcity of adequate data for model training, particularly in domains where data confidentiality is a concern. The work aimed to construct and enhance a framework that would assist researchers and developers in addressing the insufficiency of data during the data management stage. The framework incorporates various data augmentation techniques, enabling the generation of new data from the original dataset along with all the required files for detection challenges. This augmentation process improves the overall performance of ML applications by increasing both the quantity and quality of available data, thereby providing the model with the best possible input. The tool can be accessed using the following link https://github.com/TahaKh99/Image_Augmentor.

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