Machine Learning Interpretability

Machine learning has a great potential to improve data products and business processes. It is used to propose products and news articles that we might be interested in as well as to steer autonomous vehicles and to challenge human experts in non-trivial games. Although machine learning models perform extraordinary well in solving those tasks, we need to be aware of the latent risks that arise through inadvertently encoding bias, responsible for discriminating individuals and strengthening preconceptions, or mistakenly taking random correlation for causation. In her book „Weapons of Math Destruction", Cathy O'Neil even went so far as to say that improvident use of algorithms can perpetuate inequality and threaten democracy. Filter bubbles, racist chat bots, and foolable face detection are prominent examples of malicious outcomes of learning algorithms. With great power comes great responsibility--wise words that every practitioner should keep in mind.