credit: Frontiers of environmental science and engineering (2024). DOI: 10.1007/s11783-024-1825-2
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credit: Frontiers of environmental science and engineering (2024). DOI: 10.1007/s11783-024-1825-2
Environmental data science and machine learning (ML) are becoming increasingly important to address ecological challenges. However, these technologies can inadvertently perpetuate biases present in training data, leading to socioecological inequalities. The field faces challenges such as data integrity, algorithmic bias, and model overfitting, and requires deeper understanding and more unbiased approaches.
Current discussions and evolution in this field highlight the importance of incorporating equity across research and design domains to ensure fair and unbiased outcomes.
A paradigm shift to integrate socio-ecological equity with environmental data science and machine learning (ML) Frontiers of environmental science and engineering.
This paper, written by Georgia Tech's Joe F. Bozeman III, highlights the importance of understanding and addressing socio-ecological inequalities to improve the integrity of environmental data science.
In this study, we introduce and validate the Systematic Fairness Framework and Wells-Du Bois Protocol, essential tools for integrating fairness in environmental data science and machine learning. These methodologies extend beyond traditional approaches by emphasizing socio-ecological impacts along with technical precision.
Systemic equity frameworks focus on simultaneously considering distributive, procedural, and epistemic equity to ensure fair benefits for all communities, especially marginalized populations. This encourages researchers to embed equity throughout the project lifecycle, from inception to implementation.
The Wells-Du Bois Protocol provides a structured method for assessing and mitigating bias in datasets and algorithms, allowing researchers to avoid potential social bias in research that can lead to skewed results. Guides you to critically evaluate reinforcement.
“Our job is not just to improve the technology, but to make sure it serves everyone equitably,” Bozeman said. “Incorporating an equity perspective into environmental data science is critical to the integrity and relevance of our research in real-world contexts.”
This study not only highlights existing challenges in environmental data science and machine learning, but also provides practical solutions to overcome them. It sets new standards for conducting fair, unbiased, and inclusive research, thereby paving the way for more responsible and impactful environmental science practices.
For more information:
Joe F. Bozeman, Enhancing the alignment of environmental data science and machine learning requires understanding socio-ecological inequalities. Frontiers of environmental science and engineering (2024). DOI: 10.1007/s11783-024-1825-2