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Developing Bias Assessment Frameworks for Fairness in Machine Learning Models
Subject area: Science,Engineering and Technology · Area of research: Machine Learning Models
Abstract
The increasing deployment of machine learning (ML) models in critical decision-making processes raises significant concerns regarding fairness, bias, and accountability. As ML models are integrated into applications such as healthcare, criminal justice, and hiring practices, ensuring fairness is paramount to prevent discriminatory outcomes. This paper proposes a comprehensive framework for bias assessment in machine learning models, aimed at providing organizations and researchers with tools to evaluate and mitigate bias effectively. The framework incorporates both quantitative and qualitative metrics to identify potential biases in the dataset, algorithmic design, and model predictions. It takes into account diverse fairness criteria, including demographic parity, equalized odds, and individual fairness, aligning them with ethical guidelines and regulatory standards. Additionally, the framework provides a systematic approach for measuring model performance across various subgroups, helping to ensure that models deliver equitable outcomes across different demographics. The assessment tools are designed to be adaptable, allowing them to be tailored to the specific context and application of each ML model. By integrating this framework into the model development lifecycle, organizations can proactively identify and address fairness concerns, contributing to more inclusive and unbiased AI systems. This paper highlights the importance of transparent and comprehensive bias assessment, advocating for a shift toward fairness-aware ML practices to improve societal trust and the responsible use of artificial intelligence technologies.
Keywords
Bias assessment, fairness in machine learning, algorithmic bias, fairness criteria, demographic parity, equalized odds, model evaluation, ethical AI, transparent AI systems, equitable outcomes, responsible AI, AI accountability, bias mitigation, machine learning fairness framework.
How to cite this paper
@article{1706395,
author = {Pradeep Jeyachandran, Narrain Prithvi Dharuman, Suraj Dharmapuram, Dr. Sanjouli Kaushik, Prof. (Dr.) Sangeet Vashishtha; Raghav Agarwal},
title = {Developing Bias Assessment Frameworks for Fairness in Machine Learning Models},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {4},
pages = {607-640},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706395.pdf},
abstract = {The increasing deployment of machine learning (ML) models in critical decision-making processes raises significant concerns regarding fairness, bias, and accountability. As ML models are integrated into applications such as healthcare, criminal justice, and hiring practices, ensuring fairness is paramount to prevent discriminatory outcomes. This paper proposes a comprehensive framework for bias assessment in machine learning models, aimed at providing organizations and researchers with tools to evaluate and mitigate bias effectively. The framework incorporates both quantitative and qualitative metrics to identify potential biases in the dataset, algorithmic design, and model predictions. It takes into account diverse fairness criteria, including demographic parity, equalized odds, and individual fairness, aligning them with ethical guidelines and regulatory standards. Additionally, the framework provides a systematic approach for measuring model performance across various subgroups, helping to ensure that models deliver equitable outcomes across different demographics. The assessment tools are designed to be adaptable, allowing them to be tailored to the specific context and application of each ML model. By integrating this framework into the model development lifecycle, organizations can proactively identify and address fairness concerns, contributing to more inclusive and unbiased AI systems. This paper highlights the importance of transparent and comprehensive bias assessment, advocating for a shift toward fairness-aware ML practices to improve societal trust and the responsible use of artificial intelligence technologies.},
keywords = {Bias assessment, fairness in machine learning, algorithmic bias, fairness criteria, demographic parity, equalized odds, model evaluation, ethical AI, transparent AI systems, equitable outcomes, responsible AI, AI accountability, bias mitigation, machine learning fairness framework.},
month = {October},
}