This talk explores what it means to develop "grown-up" machine learning algorithms and why they are critical for applications such as medicine and healthcare. I propose three desiderata for grown-up learning algorithms: accountability for mistake/success, transparency in predictions, and flexibility in giving individuals control over their data. These properties turn out to be quite different from what is typically used or studied in machine learning and statistics. I will propose some technical definitions, example applications in healthcare and new algorithms that take an initial step in this direction. We will also teach people (briefly) how to read heart ultrasound.
Suggested Readings:
- Video-based AI for beat-to-beat assessment of cardiac function. Nature 2020. https://www.nature.com/articles/s41586-020-2145-8
- Neuron Shapley: discovering the responsible neurons. preprint 2020. https://arxiv.org/abs/2002.09815
- Making AI forget you: data deletion in machine learning. NeurIPS 2019. https://arxiv.org/abs/1907.05012
Contact kkanagaw@stanford.edu for required meeting password.
