Computer programs that are used to detect essays, job applications and other work generated by artificial intelligence can discriminate against people who are non-native English speakers, researchers say. Tests on seven popular AI text detectors found that articles written by people who did not speak English as a first language were often wrongly flagged as AI-generated, a bias that could have a serious impact on students, academics and job applicants. With the rise of ChatGPT, a generative AI program that can write essays, solve problems and create computer code, many teachers now consider AI detection as a "critical countermeasure to deter a 21st-century form of cheating", the researchers say, but they warn that the 99% accuracy claimed by some detectors is "misleading at best." Alex Hern's weekly dive in to how technology is shaping our lives Scientists led by James Zou, an assistant professor of biomedical data science at Stanford University, ran 91 English essays written by non-native English speakers through seven popular GPT detectors to see how well the programs performed. More than half of the essays, which were written for a widely recognised English proficiency test known as the Test of English as a Foreign Language, or TOEFL, were flagged as AI-generated, with one program flagging 98% of the essays as composed by AI.

Artificial Intelligence (AI) hiring tools do not reduce bias or improve diversity, researchers said in a study. The research debunks the popular myth that AI-powered recruitment software and tools can boost diversity of new hires at a workplace. Cambridge University experts who published their findings in the journal Philosophy and Technology said that companies are showing greater interest now to use AI to solve problems like interview and recruitment bias. However, they believe that the application of this AI when it analyses candidates' resumes or video is nothing but "pseudoscience". The study mentioned a 2020 survey of 500 human resource professionals from all over the world.

Pulmonary embolisms are dangerous, lung-clogging blot clots. In a pilot study, scientists at the Icahn School of Medicine at Mount Sinai showed for the first time that artificial intelligence (AI) algorithms can detect signs of these clots in electrocardiograms (EKGs), a finding which may one day help doctors with screening. The results published in the European Heart Journal – Digital Health suggested that new machine learning algorithms, which are designed to exploit a combination of EKG and electronic health record (EHR) data, may be more effective than currently used screening tests at determining whether moderate- to high-risk patients actually have pulmonary embolisms. The study was led by Sulaiman S. Somani, MD, a former medical student in the lab of Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences and a member of the Hasso Plattner Institute for Digital Health at Mount Sinai. Pulmonary embolisms happen when deep vein blood clots, usually formed in the legs or arms, break away and clog lung arteries. These clots can be lethal or cause long-term lung damage.