🔬 Research Summary by Eran Tal, Canada Research Chair in Data Ethics and Associate Professor of Philosophy at McGill University. He studies the epistemology and ethics of data collection and data use in scientific … [Read more...] about Target specification bias, counterfactual prediction, and algorithmic fairness in healthcare
Core Principles of Responsible AI
People are not coins: Morally distinct types of predictions necessitate different fairness constraints
🔬 Research Summary by Corinna Hertweck, a fourth-year PhD student at the University of Zurich and the Zurich University of Applied Sciences where she is working on algorithmic fairness. [Original paper by … [Read more...] about People are not coins: Morally distinct types of predictions necessitate different fairness constraints
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
🔬 Research Summary by Shangbin Feng, Chan Young Park, and Yulia Tsvetkov. Shangbin Feng is a Ph.D. student at University of Washington.Chan Young Park is a Ph.D. student at Carnegie Mellon University, studying … [Read more...] about From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
Confidence-Building Measures for Artificial Intelligence
🔬 Research Summary by Andrew W. Reddie, Sarah Shoker, and Leah Walker. Andrew W. Reddie is an Associate Research Professor at the University of California, Berkeley’s Goldman School of Public Policy, and Founder … [Read more...] about Confidence-Building Measures for Artificial Intelligence
From OECD to India: Exploring cross-cultural differences in perceived trust, responsibility and reliance of AI and human experts
🔬 Research Summary by Vishakha Agrawal, an independent researcher interested in human-AI collaboration, participatory AI and AI safety. [Original paper by Vishakha Agrawal, Serhiy Kandul, Markus Kneer, and Markus … [Read more...] about From OECD to India: Exploring cross-cultural differences in perceived trust, responsibility and reliance of AI and human experts