Internal Bug Promoted Problematic Content on Facebook

Facebook News allows users to access news on the US social media giant’s platform. (AFP)
Facebook News allows users to access news on the US social media giant’s platform. (AFP)
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Internal Bug Promoted Problematic Content on Facebook

Facebook News allows users to access news on the US social media giant’s platform. (AFP)
Facebook News allows users to access news on the US social media giant’s platform. (AFP)

Content identified as misleading or problematic were mistakenly prioritized in users' Facebook feeds recently, thanks to a software bug that took six months to fix, according to tech site The Verge.

Facebook disputed the report, which was published Thursday, saying that it "vastly overstated what this bug was because ultimately it had no meaningful, long-term impact on problematic content," according to Joe Osborne, a spokesman for parent company Meta.

But the bug was serious enough for a group of Facebook employees to draft an internal report referring to a "massive ranking failure" of content, The Verge reported.

In October, the employees noticed that some content which had been marked as questionable by external media -- members of Facebook's third-party fact-checking program -- was nevertheless being favored by the algorithm to be widely distributed in users' News Feeds.

"Unable to find the root cause, the engineers watched the surge subside a few weeks later and then flare up repeatedly until the ranking issue was fixed on March 11," The Verge reported.

But according to Osborne, the bug affected "only a very small number of views" of content.

That's because "the overwhelming majority of posts in Feed are not eligible to be down-ranked in the first place," Osborne explained, adding that other mechanisms designed to limit views of "harmful" content remained in place, "including other demotions, fact-checking labels and violating content removals."

AFP currently works with Facebook's fact checking program in more than 80 countries and 24 languages. Under the program, which started in December 2016, Facebook pays to use fact checks from around 80 organizations, including media outlets and specialized fact checkers, on its platform, WhatsApp and on Instagram.

Content rated "false" is downgraded in news feeds so fewer people will see it. If someone tries to share that post, they are presented with an article explaining why it is misleading.

Those who still choose to share the post receive a notification with a link to the article. No posts are taken down. Fact checkers are free to choose how and what they wish to investigate.



SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI
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SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

SDAIA, KAUST Launch MiniGPT-Med Model to Help Doctors Diagnose Medical Radiology through AI

The Center of Excellence for Data Science and Artificial Intelligence at the Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have introduced the MiniGPT-Med model.

The large multi-modal language model is designed to help doctors quickly and accurately diagnose medical radiology using artificial intelligence techniques.

Dr. Ahmed Alsinan, the Artificial Intelligence Advisor at the National Center for Artificial Intelligence and head of the scientific team at SDAIA, explained that the MiniGPT-Med model is capable of performing various tasks such as generating medical reports, answering medical visual questions, describing diseases, locating diseases, identifying diseases, and documenting medical descriptions based on entered medical images.

The model was trained on different medical images, including X-rays, CT scans, and MRIs.

The MiniGPT-Med model, derived from large-scale language models, is specifically tailored for medical applications and demonstrates significant versatility across different imaging methods, including X-rays, CT scans, and MRI. This enhances its utility in medical diagnosis.

Dr. Alsinan highlighted that the MiniGPT-Med model was developed collaboratively by artificial intelligence specialists from SDAIA and KAUST.

The model exhibits advanced performance in generating medical reports, achieving 19% higher efficiency than previous models. It serves as a general interface for radiology diagnosis, enhancing diagnostic efficiency across various medical imaging applications.