arXiv subjects

Benjamin Q. Huynh

Publications and source records attributed to Benjamin Q. Huynh.

4 recordsLinked to original sources

arXiv2026

Validity in machine learning for extreme event attribution

Extreme event attribution (EEA), which assesses the extent to which disasters are caused by climate change, is crucial for informing climate policy and legal proceedings. Machine learning is increasingly used for EEA by modeling rare weather events too complex or computationally intensive for traditional methods. However, its validity remains unclear, as machine learning applications are criticized for bias and lack of robustness. Here we evaluate machine learning for EEA using California wildfire data from 2003-2020. We identify three major threats to validity: (1) individual event attribution estimates are highly sensitive to algorithmic design choices; (2) common performance metrics like Brier score are not strongly correlated with attribution error, facilitating suboptimal model selection; and (3) distribution shift across climate scenarios substantially degrades predictive performance. We propose a more robust attribution analysis using aggregate estimates and additional evaluation metrics for predictive performance and distribution shift.

stat.AP↗

arXiv2023

AI for Anticipatory Action: Moving Beyond Climate Forecasting

Disaster response agencies have been shifting from a paradigm of climate forecasting towards one of anticipatory action: assessing not just what the climate will be, but how it will impact specific populations, thereby enabling proactive response and resource allocation. Machine learning models are becoming exceptionally powerful at climate forecasting, but methodological gaps remain in terms of facilitating anticipatory action. Here we provide an overview of anticipatory action, review relevant applications of machine learning, identify common challenges, and highlight areas where machine learning can uniquely contribute to advancing disaster response for populations most vulnerable to climate change.

cs.LG↗

arXiv2023

Potential for allocative harm in an environmental justice data tool

Neighborhood-level screening algorithms are increasingly being deployed to inform policy decisions. We evaluate one such algorithm, CalEnviroScreen - designed to promote environmental justice and used to guide hundreds of millions of dollars in public funding annually - assessing its potential for allocative harm. We observe the model to be sensitive to subjective model decisions, with 16% of tracts potentially changing designation, as well as financially consequential, estimating the effect of its positive designations as a 104% (62-145%) increase in funding, equivalent to \$2.08 billion (\$1.56-2.41 billion) over four years. We also observe allocative tradeoffs and susceptibility to manipulation, raising ethical concerns. We recommend incorporating sensitivity analyses to mitigate allocative harm and accountability mechanisms to prevent misuse.

stat.AP↗

arXiv2018

Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen

Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challenge for aid delivery organizations. Accurate forecasting of IDP migration would empower humanitarian aid groups to more effectively allocate resources during conflicts. We show that monthly flow of IDPs from province to province in both Syria and Yemen can be accurately forecasted one month in advance, using publicly available data. We model monthly IDP flow using data on food price, fuel price, wage, geospatial, and news data. We find that machine learning approaches can more accurately forecast migration trends than baseline persistence models. Our findings thus potentially enable proactive aid allocation for IDPs in anticipation of forecasted arrivals.

stat.AP↗