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ChatGPT shows geographic biases on environmental justice issues: Report

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Virginia Tech, a university in the United States, has published a report outlining potential biases in the artificial intelligence (AI) tool ChatGPT, suggesting variations in its outputs on environmental justice issues across different counties.

In a recent report, researchers from Virginia Tech have alleged that ChatGPT has limitations in delivering area-specific information regarding environmental justice issues. 

However, the study identified a trend indicating that the information was more readily available to the larger, densely populated states.

“In states with larger urban populations such as Delaware or California, fewer than 1 percent of the population lived in counties that cannot receive specific information.”

Meanwhile, regions with smaller populations lacked equivalent access.

“In rural states such as Idaho and New Hampshire, more than 90 percent of the population lived in counties that could not receive local-specific information,” the report stated.

It further cited a lecturer named Kim from Virginia Tech’s Department of Geography urging the need for further research as prejudices are being discovered.

“While more study is needed, our findings reveal that geographic biases currently exist in the ChatGPT model,” Kim declared.

The research paper also included a map illustrating the extent of the U.S. population without access to location-specific information on environmental justice issues.

A United States map showing areas where residents can view (blue) or cannot view (red) local-specific information on environmental justice issues. Source: Virginia Tech

Related: ChatGPT passes neurology exam for first time

This follows recent news that scholars are discovering potential political biases exhibited by ChatGPT in recent times.

On August 25, Cointelegraph reported that researchers from the United Kingdom and Brazil published a study that declared large language models (LLMs) like ChatGPT output text that contains errors and biases that could mislead readers and have the ability to promote political biases presented by traditional media.

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