Systematic deviations in data and model outputs in healthcare | AI for Good Discovery
0With a growing trend of employing machine learning algorithms to assist decision making, it is vital to inspect both the data and machine learning models for potential systematic deviations to achieve a trustworthy AI application. Detection of anomalous samples is a field of active research that aims to identify observations (a subgroup of samples) in a given data that deviate from some concept of normality. Its application is crucial across different domains, e.g., to understand data quality, mis-annotations, detecting adversarial attacks, monitoring model performance, and informing new data collection design. \n\nUsing healthcare as a use case, this AI for Good webinar demonstrates data-centric techniques to address specific questions, such as vulnerable groups, heterogeneous intervention effects and new class detection. Moreover, scientific discovery is being facilitated using generative models recently. However, principled evaluation of these models, in domain-agnostic and interpretable ways, is beneficial to efficiently exploit the unique generation capabilities of models. Beyond curated datasets that are often utilized to train machine learning models, data-centric analysis should also extend to traditional data sources, such as textbooks, to identify potential representation biases. \n\n Speakers:\nGirmaw Abebe Tadesse, Research Scientist, IBM Research Africa \n\n#AIforGoodDiscovery #AIforHealth\n\nJoin the Neural Network! \nhttps://aiforgood.itu.int/neural-network/\nThe AI for Good networking community platform powered by AI. \nDesigned to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\n\n Watch the latest #AIforGood videos!\n\n\nExplore more #AIforGood content:\n AI for Good Top Hits\n • Top Hits \n\n AI for Good Webinars\n • AI for Good Webinars \n\n AI for Good Keynotes\n • AI for Good Keynotes \n\n Stay updated and join our weekly AI for Good newsletter:\nhttp://eepurl.com/gI2kJ5\n\n Discover what's next on our programme!\nhttps://aiforgood.itu.int/programme/\n\nCheck out the latest AI for Good news:\nhttps://aiforgood.itu.int/newsroom/\n\nExplore the AI for Good blog:\nhttps://aiforgood.itu.int/ai-for-good-blog/\n\n Connect on our social media:\nWebsite: https://aiforgood.itu.int/\nTwitter: https://twitter.com/AIforGood\nLinkedIn Page: https://www.linkedin.com/company/26511907 \nLinkedIn Group: https://www.linkedin.com/groups/8567748 \nInstagram: https://www.instagram.com/aiforgood \nFacebook: https://www.facebook.com/AIforGood\n\nWhat is AI for Good?\nWe have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.\nMore than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.\nAI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.\n\nDisclaimer:\nThe views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.