This episode of the UAB Data Science Club, we are interviewing Patrick Hall. He has written the book on Machine Learning Interpretability, and is the Senior Director of Product at https://www.h2o.ai/.
Patrick guides us through a Disparate Impact Analysis, and we discuss AI security, fairness, and Asimov’s rules of robotics.
This is the notebook we looked at with Patrick Hall
https://nbviewer.jupyter.org/github/jphall663/interpretable_machine_learning_with_python/blob/master/dia.ipynb
Patrick Hall’s Machine Learning Interpretability Book
https://www.h2o.ai/oreilly-mli-booklet-2019/
Warning Signs: The Future of Privacy and Security in an Age of Machine Learning
https://fpf.org/wp-content/uploads/2019/09/FPF-Indecent-Exposure-Report-Final-digital.pdf
Fairness, Accountability, and Transparency in Machine Learning
https://www.fatml.org/
IBM AI Fairness 360 Toolkit
http://aif360.mybluemix.net/
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
https://arxiv.org/abs/1909.09251
