A technique to improve both fairness and accuracy in artificial intelligence
Methods that make a machine-learning model’s predictions more accurate overall can reduce accuracy for underrepresented subgroups. A new approach can help.
Exploring emerging topics in artificial intelligence policy
The second AI Policy Forum Symposium convened global stakeholders across sectors to discuss critical policy questions in artificial intelligence.
Lama Willa Baker challenges MIT audience to look beyond technology to solve the climate crises
In annual T.T. and W.F. Chao Distinguished Buddhist Lecture Series, Baker takes up “Environment, Ethics and Embodiment: Buddhist Approaches to Climate Change.”
Bringing hope and transformation to the Democratic Republic of Congo
MIT political science master’s student Milain Fayulu is building brands to bring change to his home country.
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.
3 Questions: Designing software for research ethics
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.
Learning to think critically about machine learning
A multidisciplinary team of graduate students helps infuse ethical computing content into MIT’s largest machine learning course.
Dan Huttenlocher ponders our human future in an age of artificial intelligence
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.
How artificial intelligence can help combat systemic racism
MLK Visiting Professor S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.
A new resource for teaching responsible technology development
The Social and Ethical Responsibilities of Computing publishes a collection of original pedagogical materials developed for instructional use on MIT OpenCourseWare.
Can machine-learning models overcome biased datasets?
A model’s ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.
Fostering media literacy in the age of deepfakes
Online course from the MIT Center for Advanced Virtuality seeks to empower students and educators to critically engage with media.
3 Questions: Women’s rights and rising threats to press freedom worldwide
Polish journalist Ada Petriczko, an Elizabeth Neuffer Fellow at MIT, discusses ethical and cross-border journalism, freedom of speech, and the rise of autocracy.
The downside of machine learning in health care
Assistant Professor Marzyeh Ghassemi explores how hidden biases in medical data could compromise artificial intelligence approaches.
The promise and pitfalls of artificial intelligence explored at TEDxMIT event
MIT scientists discuss the future of AI with applications across many sectors, as a tool that can be both beneficial and harmful.