Dark Side of Data Science - STUDENTS ONLY

Dark Side of Data Science – review of selected examples from famous book Weapons of Math Destruction.

Algorithms, data science, machine learning and AI are the biggest buzzwords of our times and get a lot of excitement and attention. However, there is also a dark side of algorithms. The session delivered by Justina Ivanauskaite and Michal Hakala will cover interesting and shocking examples where algorithms for various reasons brought harm to society.

Justina Ivanauskaite
She is an experienced data scientist with a background in statistics. Her expertise lies primarily in econometric modeling, statistics, and simulation. Currently, she is data science lead of Animal Health Advanced Analytics team, which supports research, new product development, manufacturing, and commercial aspects of animal health in MSD. Justina is interested in creating data science solutions with an emphasis on reusability and reproducibility, which delivers value to the client. Justina is interested in creating data science solutions that bring value to the client, with emphasis on reusability and reproducibility.

Michal Hakala
Michal is statistician and econometrician focusing mainly on econometrics, predictive modeling, and risk-neutral asset pricing. His previous working experience includes developing time-continuous asset pricing models for insurance companies as an actuarial science consultant. Currently, he is working as a data scientist in MSD, and supporting manufacturing and commercial aspects of animal health. Michal is also a PhD candidate at CERGE-EI, joint workplace of Charles University and Czech Academy of Sciences, with a research focus on developing semi-parametric methods for predicting conditional distributions of high-frequency asset returns.