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Machine learning is critical for decision-making and policy operations across various domains, but it also raises fairness concerns regarding discrimination and bias. Recent government actions, including President Biden's executive order advancing racial equity, emphasize the need for responsible AI. The evolving landscape of machine learning is significantly influenced by larger models, improved computational power, and extensive datasets, which can exacerbate disparities. Various techniques, like network pruning and hardware selection, potentially exacerbate unfairness. The talk focuses on understanding the sources of these biases and proposes frameworks to mitigate them, ensuring equitable AI applications across different groups.
Machine learning's role in decision-making raises critical fairness concerns and biases.
Larger models and better compute bring challenges in efficiency and fairness.
Model deployment often lacks consideration for context-specific application constraints.
Hardware selection significantly affects model performance and can amplify biases.
Analyzing loss function characteristics reveals links to fairness disparities across groups.
Addressing the ethical implications of machine learning, the focus must be on how fairness can be assessed concerning discriminatory practices. The observed biases can stem from various stages in model training, evaluation, and deployment, emphasizing the need for a framework that ensures systematic governance over AI systems. For instance, the recent executive order on racial equity demonstrates the commitment required from the government to ensure that machine learning applications serve all demographic groups fairly.
The varying performance of models across different hardware highlights a critical data science challenge: the sensitivity of algorithms to computational environments. As shown in the experiments, disparities in performance can significantly affect decision outcomes, particularly for marginalized groups. This necessitates a thorough review and potential redesign of data science practices to embed fairness considerations directly into model development and evaluation workflows.
This model significantly influences decision-making processes in various fields, including law and finance.
The talk emphasizes the critical need to address bias to ensure fairness in model outcomes.
It has been shown to exacerbate disparities in model accuracy across different demographic groups.
Faculty and students from the university collaborated on the presented work regarding fairness in AI.
NYU's collaboration in the study highlights its commitment to exploring ethical implications in technology.
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