(Press-News.org) In your coverage, please use this URL to provide access to the freely available article in PLOS Digital Health: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000278
Article Title: Bias in artificial intelligence algorithms and recommendations for mitigation
Author Countries: Jordan, United States, Canada
Funding: The authors received no specific funding for this work.
END
Bias in AI algorithms could be mitigated by implementing new checklist
2023-06-22
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Africa's share of global HIV research output has increased from 5.1% to 31.3% over the last 35 years, but is still low compared to its relative burden of infections
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Article URL: https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0000544
Article Title: HIV research output in African Countries between 1986–2020
Author Countries: Nigeria, USA, UK
Funding: The authors received no specific funding for this work. END ...
How might generative AI models distort human beliefs?
2023-06-22
Generative AI models such as ChatGPT, DALL-E, and Midjourney all have features that may distort human beliefs through their transmission of false information and stereotyped biases, according to Celeste Kidd and Abeba Birhane. In this Perspective, they discuss how research on human psychology can explain why generative AI could be particularly powerful in distorting beliefs. The capabilities of generative AI have been exaggerated at this point, they suggest, leading to a belief that these models exceed human capabilities. People are predisposed to adopt the information of knowledgeable, confident agents like generative AI ...
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Generative AI models are encoding biases and negative stereotypes in their users
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The likes of ChatGPT, Google’s Bard and Midjourney can also help spread incorrect, nonsensical information
Marginalised groups are disproportionately affected
Children are at particular risk
In the space of a few months generative AI models, such as ChatGPT, Google’s Bard and Midjourney, have been adopted by more and more people in a variety of professional and personal ways. But growing research is underlining that they are encoding biases and negative stereotypes in their users, as well as mass generating and spreading seemingly accurate but nonsensical information. Worryingly, marginalised groups are disproportionately affected by the fabrication of this nonsensical information.
In ...