All articles tagged with #biases
A flawed study claiming vaccines cause chronic illnesses in children is critically analyzed, revealing significant biases such as surveillance bias, detection bias, and confounding factors, which undermine its conclusions. The study's design issues, including unequal follow-up times and differences in healthcare utilization, prevent it from establishing a causal link between vaccines and long-term health risks.
Computer scientist Joy Buolamwini, founder of the Algorithmic Justice League, warns about the biases and social implications of facial analysis systems in her new book, "Unmasking AI: My Mission to Protect What Is Human in a World of Machines." Buolamwini highlights how facial recognition software often fails to detect dark-skinned individuals and misgenders women, perpetuating stereotypes and reinforcing existing biases. She urges everyone to question the adoption and functionality of AI systems, emphasizing the need for representation and inclusivity in the conversation about AI. Buolamwini also calls for the protection of biometric rights and the safeguarding of civil and human rights in the face of AI advancements.
Dr. Matthew Harris, a public health researcher at Imperial College London, discusses his book "Decolonizing Healthcare Innovation: Low-Cost Solutions from Low-Income Countries" and the biases that prevent researchers and clinicians from the Global South from being heard in the field of global health. He highlights the importance of learning from low-income countries and their frugal innovations in healthcare, which often deliver similar outcomes at a fraction of the cost. Harris also emphasizes the need for both soft and radical reforms to decolonize global health and create a more equitable knowledge ecosystem.
Artificial intelligence (AI) is being increasingly utilized in cancer detection and treatment, with machine-learning tools being developed to catch tumors or lesions that may be missed by doctors. AI is also being used to assist in cancer treatment and communication with patients. However, there are concerns about biases in detecting cancers in patients of different races and the potential for overdiagnosis. Experts emphasize the need for careful consideration and collaboration between clinicians, researchers, and patients to determine the appropriate use of AI in cancer diagnosis and treatment.
AI-generated images of people from various US cities have been created using generative AI image models. However, it is important to note that these images are not accurate or comprehensive representations of human experience, as they reflect the biases and stereotypes embedded within AI models.
Former Google AI researcher Timnit Gebru has warned that the current AI "gold rush" means companies won't "self-regulate" unless they are pressured. Gebru, who was fired from Google after pointing out biases in AI, said that regulation is needed and that a profit motive is not enough. She also stated that safeguards are being cast aside due to the hype around AI.