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Interpretable Machine Learning

August 1, 2022

Serg Masis talks about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems, the different types of technical and non-technical solutions to tackling bias, the future of machine learning interpretability, and much more. 

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Key Takeaways


The three main challenges in interpretable machine learning are fairness, accountability, and transparency.


The best way to assess risk is to view machine learning models as systems with different factors that interact with each other. This prioritizes experimentation, not just inference or prediction, to determine how different aspects of the model impact each other and the outcome.


As machine learning systems become more efficient and take less time to develop, model interpretability and improvement will become more central to the data scientist role.

Key Quotes

The three challenges of interpretable machine learning are fairness, accountability, and transparency. First, fairness addresses discernable biases and discrimination in a model. Second, accountability addresses robustness, consistency, traceability, and privacy, ensuring that models can be relied on over time. Lastly, transparency addresses explainability and understanding of how decisions were made and how the model connects inputs to outputs. You cannot prove a model is robust or fair without transparency, and an explainable model that lacks fairness or reliability is a failed model.

As AI is adopted in different ways, many are so worried about the potential dangers that they fail to identify the potential benefits. People treated the internet similarly, and didn’t realize how data could be exploited, stolen identities, or spread misinformation. But little by little, everything became more robust, such as with the adoption of SSL. Early in the internet’s adoption, people started working to protect the internet as a technology for those that do not want to use it for dangerous means, and while people do still use it inappropriately, it has also enabled amazing societal transformation at a rate exponentially faster than anything seen before it.

About Serg Masis

Photo of Serg Masis

Serg Masis is a Climate & Agronomic Data Scientist at Syngenta and the author of the book, Interpretable Machine Learning with Python. Serg has developed his expertise in Interpretable Machine Learning, Explainable AI, Behavioral Economics, Causal Inference, and Responsible/Ethical AI throughout his career, which spans web and software development, mobile app development, systems analyst, ML engineer, and more.

Photo of Adel Nehme
Meet our host
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

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