Wiley shuts 19 scholarly journals amid AI paper mill problems; The Register, May 16, 2024

 Thomas Claburn, The Register; Wiley shuts 19 scholarly journals amid AI paper mill problems

"US publishing house Wiley this week discontinued 19 scientific journals overseen by its Hindawi subsidiary, the center of a long-running scholarly publishing scandal.

In December 2023 Wiley announced it would stop using the Hindawi brand, acquired in 2021, following its decision in May 2023 to shut four of its journals "to mitigate against systematic manipulation of the publishing process."

Hindawi's journals were found to be publishing papers from paper mills – organizations or groups of individuals who try to subvert the academic publishing process for financial gain. Over the past two years, a Wiley spokesperson told The Register, the publisher has retracted more than 11,300 papers from its Hindawi portfolio.

As described in a Wiley-authored white paper published last December, "Tackling publication manipulation at scale: Hindawi’s journey and lessons for academic publishing," paper mills rely on various unethical practices – such as the use of AI in manuscript fabrication and image manipulations, and gaming the peer review process...

In January, Wiley signed on to United2Act – an industry initiative to combat paper mills.

But the concern over scholarly research integrity isn't confined to Wiley publications. A study published in Nature last July suggests as many as a quarter of clinical trials are problematic or entirely fabricated."

Inside the Deepfake ‘Arms Race’; The Daily Beast, October 7, 2019

David Axe, The Daily Beast; Inside the Deepfake ‘Arms Race’

Can countermeasures neutralize the coming wave of high-tech disinformation?

"The first deepfakes appeared in late 2017 on Reddit. An anonymous user calling themselves “deepfakes”—a portmanteau of artificial-intelligence “deep learning” and “fakes”—imposed celebrities’ faces on pornography...

A deepfake video, still image, or audio recording is the product of a clever bit of coding called a “generative adversarial network,” or GAN. A GAN has two components: a discriminator and a generator. The discriminator is trying to tell fake media from real media. The generator is trying to fool the discriminator with increasingly realistic-seeming fakes."