Interpretable Machine Learning with Serg Masis

Serg Masis is the author of best-selling book 'Interpretable Machine Learning with Python' and senior Data Scientist at Sygenta. He has mentored many data scientists around the world.  Timestamps: 00:00 intro 08:30  Old 4.77 MH  z Computer, Late 80s and Programming 11:51 Fairness, Accountability and Transparency in Machine Learning, Startup and Harvard 16:33  Fairness vs Preciseness, Bias and Variance Tradeoff, Are Engineers to blame? 21:43 Mask-Detection Problem in Coded-Bias, Biased Samples,  Surveillance using CV 32:38 Fixing Biased Datasets, Augmenting Data and Limitations  37:39 Algorithmic Optimisation and Explainability 40:51 Eric Schmidt on Behavioral Prediction, SHAP values, Tree and DeepExplainers 44:50 Challenges of using SHAP and LIME & Big Data 49:37 GPT3, Large Models and ROI on Explainability 01:00:00  TCAS, Collision Risks and Interpretability, Ransom Attacks 01:08:09 Guitar, Bass, and Led Zepplin 01:09:31 Birth Order and IQ, Science vs Folk Wisdom 01:13:30  Reverse Discrimination & Men, Bias in Child Custody, Prison Sentences, and Incarceration 01:23:11 Receidivism to Criminal Behaviour, Ethnic over-representation & Systematic Racism 01:24:44  Human Judges vs AI,  Absolute Fairness, Food and Parole 01:30:20  Face Detection in China, Privacy vs Convenience, Feature Engineering and Model Parsimony  01:35:51 Sparsity, Interaction Effects, and Multicollinearity 01:38:23  Four levels of Global and Local Predictive Explainability 01:43:17  Recursive and Sequential Feature Selection 01:47:42  Ensemble, Blended and Stacked Models and Interpretability 01:53:45  In-Processing and Post-Processing Bias Mitigation 01:57:00  Future of Interpretable AI