Welcome to the Feb’20 chapter of the Düsseldorf Data Science Meetup. At this session, Omayma Said will talk about Machine Learning Interpretability.
Machine Learning Interpretability: Why and How?
With the increasing adoption of machine learning-based solutions in different domains, systems that use black-box algorithms are getting used more often with the promise of providing better performance. However, this improvement in performance comes at the cost of interpretability, which introduces a barrier against the wider adoption of such algorithms in crucial areas and raises the scepticism of the impacted individuals.
This session will focus on the importance of interpretable machine learning, why it is crucial from technical and ethical perspectives and its current limitations. In addition, we will go over some of the relevant tools/packages (e.g. LIME, SHAPLEY) and discuss some real-life use cases.
About the Speaker
Omayma is a Senior Data Scientist with a background in electronics and telecommunication engineering. She worked in several roles where she dealt with different types of data, built data products, and previously led a data science team.
In the world of data science, she is most interested in reproducible workflows, fairness in data products and explainable AI. She also enjoys teaching coding and data-science related skills and she is a certified instructor from The Carpentries and Rstudio.
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