Bias in the collection of data on which artificial intelligence (AI) computer programs depend can limit the usefulness of this rapidly growing tool for climate scientists predicting future scenarios and guiding global action, according to a new paper by researchers at the University of Cambridge, published in npj Climate Action .
are trained to trawl through complex data sets looking for patterns and insightful information. However, missing information from certain locations on the planet, time periods, or societal dynamics create"holes" in the data that can lead to unreliable climate predictions and misleading conclusions.
By contrast, those without the same access to technology, such as Indigenous communities in the Global South, are more likely to find their experiences, perceptions and priorities missing from those same digital sources. "Biased" AI has the potential to misrepresent climate information. For example, it could generate ineffective weather predictions or underestimate carbon emissions from certain industries, which could then misguide governments trying to create policy and regulations aimed at mitigating or adapting to climate change.
However, these"data holes" can be filled by human knowledge. The authors advocate for a human-in-the loop design to offer AIprograms with a sense check on which data is used and the context in which it is used, in an effort to improve the accuracy of predictions and the usefulness of any conclusions.
France Dernières Nouvelles, France Actualités
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