How human-AI feedback loops alter human perceptual, emotional and social judgements.

Artificial intelligence (AI) technologies are rapidly advancing, enhancing human capabilities across various fields spanning from finance to medicine. Despite their numerous advantages, AI systems can exhibit biased judgements in domains ranging from perception to emotion. Here, in a series of experiments (n = 1,401 participants), we reveal a feedback loop where human-AI interactions alter processes underlying human perceptual, emotional and social judgements, subsequently amplifying biases in humans.

Three diverse motives for information sharing.

Knowledge is distributed over many individuals. Thus, humans are tasked with informing one another for the betterment of all. But as information can alter people's action, affect and cognition in both positive and negative ways, deciding whether to share information can be a particularly difficult problem.

AI-induced hyper-learning in humans.

Humans evolved to learn from one another. Today, however, learning opportunities often emerge from interactions with AI systems. Here, we argue that learning from AI systems resembles learning from other humans, but may be faster and more efficient.

Tuberculosis in otherwise healthy adults with inherited TNF deficiency.

Severe defects in human IFNγ immunity predispose individuals to both Bacillus Calmette-Guérin disease and tuberculosis, whereas milder defects predispose only to tuberculosis. Here we report two adults with recurrent pulmonary tuberculosis who are homozygous for a private loss-of-function TNF variant. Neither has any other clinical phenotype and both mount normal clinical and biological inflammatory responses.

A global survey on the use of the international classification of diseases codes for metabolic dysfunction-associated fatty liver disease.

Background: With the implementation of the 11th edition of the International Classification of Diseases (ICD-11) and the publication of the metabolic dysfunction-associated fatty liver disease (MAFLD) nomenclature in 2020, it is important to establish consensus for the coding of MAFLD in ICD-11. This will inform subsequent revisions of ICD-11.

Methods: Using the Qualtrics XM and WJX platforms, questionnaires were sent online to MAFLD-ICD-11 coding collaborators, authors of papers, and relevant association members.