Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender. In this work, we introduce a comprehensive benchmark that is meant to capture the amplification of social bias, via stigmas, in generative language models. Taking inspiration from social science research, we start with a documented list of 93 US-centric stigmas and curate a question-answering (QA) dataset which involves simple social situations. Our benchmark, SocialStigmaQA, contains roughly 10K prompts, with a variety of prompt styles, carefully constructed to systematically test for both social bias and model robustness. We present results for SocialStigmaQA with two open source generative language models and we find that the proportion of socially biased output ranges from 45% to 59% across a variety of decoding strategies and prompting styles. We demonstrate that the deliberate design of the templates in our benchmark (e.g., adding biasing text to the prompt or using different verbs that change the answer that indicates bias) impacts the model tendencies to generate socially biased output. Additionally, through manual evaluation, we discover problematic patterns in the generated chain-of-thought output that range from subtle bias to lack of reasoning. Warning: This paper contains examples of text which are toxic, biased, and potentially harmful.

Artificial intelligence is a much-discussed topic in medicine, especially in the field of diagnostics. "We aimed to investigate the potential on the basis of a specific example," explains Prof. Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "Because this requires large amounts of data, we evaluated data on the gene activity of blood cells. Numerous studies have been carried out on this topic and the results are available through databases. Thus, there is an enormous data pool. We have collected virtually everything that is currently available."

Artificial intelligence is a much-discussed topic in medicine, especially in the field of diagnostics. "We aimed to investigate the potential on the basis of a specific example," explains Prof. Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "Because this requires large amounts of data, we evaluated data on the gene activity of blood cells. Numerous studies have been carried out on this topic and the results are available through databases. Thus, there is an enormous data pool. We have collected virtually everything that is currently available."

Artificial intelligence can detect one of the most common forms of blood cancer - acute myeloid leukemia (AML) - with high reliability. Researchers at the German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn have now shown this in a proof-of-concept study. Their approach is based on the analysis of the gene activity of cells found in the blood. Used in practice, this approach could support conventional diagnostics and possibly accelerate the beginning of therapy. The research results have been published in the journal "iScience".

How long until Alexa and Siri, our robot companions, know more about our health than our family doctor? Artificial intelligence, often referred to as machine intelligence, is all around us. Our smartphones, our social media apps, every time we shop online, our in-home robot companions, are all driven by AI. If Alexa and Siri know more about your day to day habits, emotions, and atmospheric influences, it's only a matter of time before our in-home robots, with access to more data, will know more than just our overall health. This plethora of data will be a gold mine for those businesses that own it and choose the right application for its use.

Primary care serves as patients' first point of contact with the healthcare system and is a continuing focal point of comprehensive, accessible, and community-based care [1]. More than just a gate-keeping process for specialist referrals, it has been widely recognized for its focus on caring for the longterm health of patients rather than solely for treating specific diseases or conditions. As such, primary care helps deliver more equitable health outcomes across populations and meets 80-90% of individuals' health needs throughout their lives [2]. To this end, a recent special report from the Economist stated that "good primary care is an essential precondition for a decent healthcare system" [3]. The World Health Organization (WHO) emphasized several defining features for effective and socially productive primary care, including comprehensiveness, person-centeredness, and continuity of care [4]. In particular, person-centeredness refers to the "clinical method of participatory democracy" that allows patients to participate in decisions that affect their health.

HANGZHOU, China/SHANGHAI, June 28 (Reuters) - In the eastern Chinese city of Hangzhou, an ambulance speeds through traffic on a wave of green lights, helped along by an artificial intelligence (AI) system and big data. The system, which involves sending information to a centralised computer linked to the city's transport networks, is part of a trial by Alibaba Group Holding Ltd. The Chinese tech giant is hoping to use its cloud and data systems to tackle issues hobbling China's healthcare system like snarled city traffic, long patient queues and a lack of doctors. Alibaba's push into healthcare reflects a wider trend in China, where technology firms are racing to shake up a creaking state-run health sector and take a slice of spending that McKinsey & Co estimates will hit $1 trillion by 2020. Tencent-backed WeDoctor, which offers online consultations and doctor appointments, raised $500 million in May at a valuation of $5.5 billion.

HANGZHOU, China/SHANGHAI (Reuters) - In the eastern Chinese city of Hangzhou, an ambulance speeds through traffic on a wave of green lights, helped along by an artificial intelligence (AI) system and big data. The system, which involves sending information to a centralized computer linked to the city's transport networks, is part of a trial by Alibaba Group Holding Ltd. The Chinese tech giant is hoping to use its cloud and data systems to tackle issues hobbling China's healthcare system like snarled city traffic, long patient queues and a lack of doctors. Alibaba's push into healthcare reflects a wider trend in China, where technology firms are racing to shake up a creaking state-run health sector and take a slice of spending that McKinsey & Co estimates will hit $1 trillion by 2020. Tencent-backed WeDoctor, which offers online consultations and doctor appointments, raised $500 million in May at a valuation of $5.5 billion.

LONDON (Reuters) - London-based Babylon Health says its artificial intelligence technology, in tests, has outperformed most physicians in assessing disease symptoms, throwing down a challenge to doctors, some of whom doubt its true abilities. Babylon, which was founded by entrepreneur Ali Parsa in 2013, is one of a number of start-ups tapping into the promise of artificial intelligence (AI) to help patients and doctors sift through symptoms to come up with a diagnosis. It aims to offer health advice of family doctor quality by using AI delivered through a smartphone chatbot app - potentially a big saving for governments as they struggle to fund healthcare for growing and ageing populations. In a representative sample of questions set by the Royal College of General Practitioners (RCGP) for its final exams to qualify as a family doctor, the Babylon app achieved an 81 percent success level, well ahead of the average pass mark over the last five years of 72 percent, the company said. But Martin Marshall, vice chairman of the RCGP, said AI systems could not be compared to highly-trained medical professionals.