Despite the pervasive deployment of electronic health record systems, their promise to streamline entering and retrieving clinical data, and the recent advances in artificial intelligence in healthcare, getting meaningful and actionable information at the point of care is still a formidable challenge for clinicians. In this talk, I will present our approach to designing, building, and deploying tools to support clinicians in their decision-making workflow, as well as facilitating the patient-provider partnership in shared-decision making.
The large amount of language available online today makes it possible to think about how to learn from this language to help address needs faced by society. In this talk, I will describe research in our group on summarization and social media analysis that addresses several different challenges. We have developed approaches that can be used to help people live and work in today’s global world, approaches to help determine where problems lie following a disaster, and approaches to identify when the social media posts of gang-involved youth in Chicago express either aggression or loss.
Introduction to Methods / Look for the Helpers: Creating and Maintaining a Culture of Allyship and Advocacy in Computing
Machine learning is now a general-purpose technology. In many domains, we can build models to support important decisions or automate routine tasks. Yet we may not reap their benefits due to disuse, or inflict harm due to misuse. In this talk, I will present methodological advances that address these "last mile" challenges in healthcare applications. First, I will describe a method to learn simple risk scores that are readily adopted for medical decision support, and discuss applications to adult ADHD diagnosis and ICU seizure prediction.
The past few years have seen a startling and troubling rise in the fake-news phenomena in which everyone from individuals to state-sponsored entities can produce and distribute mis-information. The implications of fake news range from a mis-informed public to an existential threat to democracy, and horrific violence. At the same time, recent and rapid advances in machine learning are making it easier than ever to create sophisticated and compelling fake images and videos, making the fake-news phenomena even more powerful and dangerous.
I will present the 'virtual democracy' framework for the design of ethical AI. In a nutshell, the framework consists of three steps: first, collect preferences from voters on example dilemmas; second, learn models of their preferences, which generalize to any (previously unseen) dilemma; and third, at runtime, predict the voters' preferences on the current dilemma, and aggregate these virtual 'votes' using a voting rule to reach a decision.