Digital ethics has become a more and more important topic, and is highly
relevant also when it comes to machine learning. Biased training data
(e.g. gender, racial bias, and more) can have dramatic consequences for
the fairness of applications using machine learning models. When a model
trained on biased data is used for smart decision making, unfair
decisions might be taken. We therefore need a transparent and
independent classification system, to measure the fairness of training
data fed into machine learning algorithms.
Speakers' biography :
Dr.
Mascha Kurpicz-Briki obtained her PhD in the area of energy-efficient
cloud computing at the University of Neuchâtel in 2016. After her PhD,
she worked a few years in industry, in the area of open-source
engineering, cloud computing and analytics. She is now professor for
data engineering at Bern University of Applied Sciences, investigating
how to make the digital society ethical and fair and applying digital
methods to social and community challenges.
Twitter:
@SocietyData
