16 research outputs found
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AI & Agency
In July of 2019, at the Summer Institute on AI and Society in Edmonton, Canada (co-sponsored by CIFAR and the AI Pulse Project of UCLA Law), scholars from across disciplines came together in an intensive workshop. For the second half of the workshop, the cohort split into smaller working groups to delve into specific topics related to AI and Society.I proposed deeper exploration on the topic of “agency,” which is defined differently across domains and cultures, and relates to many of the topics of discussion in AI ethics, including responsibility and accountability. It is also the subject of an ongoing art and research project I’m producing. As a group, we looked at definitions of agency across fields, found paradoxes and incongruities, shared our own questions, and produced a visual map of the conceptual space. We decided that our disparate perspectives were better articulated through a collection of short written pieces, presented as a set, rather than a singular essay on the topic. The outputs of this work are shared here.This set of essays, many of which are framed as provocations, suggests that there remain many open questions, and inconsistent assumptions on the topic. Many of the writings include more questions than answers, encouraging readers to revisit their own beliefs about agency. As we further develop AI systems, and refer to humans and non-humans as “agents”– we will benefit from a better understanding of what we mean when we call something an “agent” or claim that an action involves “agency.” This work is under development and many of us will continue to explore this in our ongoing AI work. – Sarah Newman, Project Lead, August 201
An open challenge to advance probabilistic forecasting for dengue epidemics.
A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue
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Technocultural Pluralism
t the end of the Cold War, the renowned political scientist, Samuel Huntington, argued that future conflicts were more likely to stem from cultural frictions– ideologies, social norms, and political systems– rather than political or economic frictions. Huntington focused his concern on the future of geopolitics in a rapidly shrinking world. But his argument applies as forcefully (if not more) to the interaction of technocultures