Given what we know, how do we live now?
MIT's Council for the Uncertain Human Future convenes small circle groups to reckon with the climate crisis in solidarity.
MIT's Council for the Uncertain Human Future convenes small circle groups to reckon with the climate crisis in solidarity.
PhD candidate Jonathan Zong found a lack of systems that earn and maintain public trust in large-scale online research — so he made one himself.
A multidisciplinary team of graduate students helps infuse ethical computing content into MIT’s largest machine learning course.
For the MIT Schwarzman College of Computing dean, bringing disciplines together is the best way to address challenges and opportunities posed by rapid advancements in computing.
MLK Visiting Professor S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.
The Social and Ethical Responsibilities of Computing publishes a collection of original pedagogical materials developed for instructional use on MIT OpenCourseWare.
A model’s ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.
Online course from the MIT Center for Advanced Virtuality seeks to empower students and educators to critically engage with media.
Polish journalist Ada Petriczko, an Elizabeth Neuffer Fellow at MIT, discusses ethical and cross-border journalism, freedom of speech, and the rise of autocracy.
Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches.
MIT scientists discuss the future of AI with applications across many sectors, as a tool that can be both beneficial and harmful.
In 2.C01, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions.
PhD student's research centers on ethics, including bioethics, and the philosophy of action.
Probabilistic programming language allows for fast, error-free answers to hard AI problems, including fairness.
Advancing the study and practice of thinking responsibly in computing education, research, and implementation.