Machine Learning

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.

HAI Senior Fellow Yejin Choi discussed responsible AI model training at Davos, asking, “What if there could be an alternative form of intelligence that really learns … morals, human values from the get-go, as opposed to just training LLMs on the entirety of the internet, which actually includes the worst part of humanity, and then we then try to patch things up by doing ‘alignment’?” 

Current societal trends reflect an increased mistrust in science and a lowered civic engagement that threaten to impair research that is foundational for ensuring public health and advancing health equity. One effective countermeasure to these trends lies in community-facing citizen science applications to increase public participation in scientific research, making this field an important target for artificial intelligence (AI) exploration. We highlight potentially promising citizen science AI applications that extend beyond individual use to the community level, including conversational large language models, text-to-image generative AI tools, descriptive analytics for analyzing integrated macro- and micro-level data, and predictive analytics. The novel adaptations of AI technologies for community-engaged participatory research also bring an array of potential risks. We highlight possible negative externalities and mitigations for some of the potential ethical and societal challenges in this field.

This brief examines the debate on algorithmic fairness in clinical predictive algorithms and recommends paths to safer, more equitable healthcare AI.

Axios chief technology correspondent Ina Fried speaks to HAI Senior Fellow Yejin Choi at Axios House in Davos during the World Economic Forum.