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Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support
Various tools and practices have been developed to support practitioners in identifying, assessing, and mitigating fairness-related harms caused by AI systems.
Transfer learning to improve streamflow forecasts in data sparse regions
In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions.
Proposing an Interactive Audit Pipeline for Visual Privacy Research
Our goal is to systematically analyze the machine learning pipeline for visual privacy and bias issues.
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