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The spread of infectious diseases is a highly complex spatiotemporal process, difficult to understand, predict, and effectively respond to. Machine learning and artificial intelligence (AI) have achieved impressive results in other learning and prediction tasks; however, while many AI solutions are developed for disease prediction, only a few of them are adopted by decision-makers to support policy interventions. Among several issues preventing their uptake, AI methods are known to amplify the bias in the data they are trained on. This is especially problematic for infectious disease models that typically leverage large, open, and inherently biased spatiotemporal data. These biases may propagate through the modeling pipeline to decision-making, resulting in inequitable policy interventions. Therefore, there is a need to gain an understanding of how the AI disease modeling pipeline can mitigate biased input data, in-processing models, and biased outputs. Specifically, our vision is to develop a large-scale micro-simulation of individuals from which human mobility, population, and disease ground-truth data can be obtained. From this complete dataset—which may not reflect the real world—we can sample and inject different types of bias. By using the sampled data in which bias is known (as it is given as the simulation parameter), we can explore how existing solutions for fairness in AI can mitigate and correct these biases and investigate novel AI fairness solutions. Achieving this vision would result in improved trust in such models for informing fair and equitable policy interventions.
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A Framework for Measuring and Benchmarking Fairness of Generative Crowd-Flow Models
Urban population growth has significantly complicated the management of mobility systems, demanding innovative tools for planning. Generative Crowd-Flow (GCF) models, which leverage machine learning to simulate urban movement patterns, offer a promising solution but lack sufficient evaluation of their fairness–a critical factor for equitable urban planning. We present an approach to measure and benchmark the fairness of GCF models by developing a first-of-its-kind set of fairness metrics specifically tailored for this purpose. Using observed flow data, we employ a stochastic biased sampling approach to generate multiple permutations of Origin-Destination datasets, each demonstrating intentional bias. Our proposed framework allows for the comparison of multiple GCF models to evaluate how models introduce bias in outputs. Preliminary results indicate a tradeoff between model accuracy and fairness, underscoring the need for careful consideration in the deployment of these technologies. To this end, this study bridges the gap between human mobility literature and fairness in machine learning, with potential to help urban planners and policymakers leverage GCF models for more equitable urban infrastructure development.
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- Award ID(s):
- 2304213
- PAR ID:
- 10606402
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- ACM Journal on Computing and Sustainable Societies
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2834-5533
- Format(s):
- Medium: X Size: p. 1-27
- Size(s):
- p. 1-27
- Sponsoring Org:
- National Science Foundation
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