All Work Published on Machine Learning
The Stanford HAI co-founder is recognized for breakthroughs that propelled computer vision and deep learning, and for championing human-centered AI and industry innovation.
The Stanford HAI co-founder is recognized for breakthroughs that propelled computer vision and deep learning, and for championing human-centered AI and industry innovation.
Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such challenges range from spurious associations between variables in medical studies to the bias of race in gender or face recognition systems. Controlling for all types of biases in the dataset curation stage is cumbersome and sometimes impossible. The alternative is to use the available data and build models incorporating fair representation learning. In this paper, we propose such a model based on adversarial training with two competing objectives to learn features that have (1) maximum discriminative power with respect to the task and (2) minimal statistical mean dependence with the protected (bias) variable(s). Our approach does so by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and the learned features. We apply our method to synthetic data, medical images (containing task bias), and a dataset for gender classification (containing dataset bias). Our results show that the learned features by our method not only result in superior prediction performance but also are unbiased.
Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such challenges range from spurious associations between variables in medical studies to the bias of race in gender or face recognition systems. Controlling for all types of biases in the dataset curation stage is cumbersome and sometimes impossible. The alternative is to use the available data and build models incorporating fair representation learning. In this paper, we propose such a model based on adversarial training with two competing objectives to learn features that have (1) maximum discriminative power with respect to the task and (2) minimal statistical mean dependence with the protected (bias) variable(s). Our approach does so by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and the learned features. We apply our method to synthetic data, medical images (containing task bias), and a dataset for gender classification (containing dataset bias). Our results show that the learned features by our method not only result in superior prediction performance but also are unbiased.
By having AI study a user’s context offline, researchers dramatically reduce the memory and cost required to make AI contextually aware.
By having AI study a user’s context offline, researchers dramatically reduce the memory and cost required to make AI contextually aware.
With a first-of-its-kind competition for roboticists everywhere, researchers at Stanford are hoping to push domestic robotics into a new age of autonomy and capability.
With a first-of-its-kind competition for roboticists everywhere, researchers at Stanford are hoping to push domestic robotics into a new age of autonomy and capability.
Stanford AI Scholars Find Support for Innovation in a Time of Uncertainty
Stanford HAI offers critical resources for faculty and students to continue groundbreaking research across the vast AI landscape.
Stanford HAI offers critical resources for faculty and students to continue groundbreaking research across the vast AI landscape.
Digital Twins Offer Insights into Brains Struggling with Math — and Hope for Students
Researchers used artificial intelligence to analyze the brain scans of students solving math problems, offering the first-ever peek into the neuroscience of math disabilities.
Researchers used artificial intelligence to analyze the brain scans of students solving math problems, offering the first-ever peek into the neuroscience of math disabilities.
