When Recommendations Systems Go Bad: Machine learning and recommendations systems have changed the way we interact with not just the internet, but some of the basic products and services that we use to run our lives. While the reach and impact of big data and algorithms will continue to grow, how do we ensure that people are treated justly? Certainly there are already algorithms in use that determine if someone will receive a job interview or be accepted into a school. Misuse of data in many of these cases could have serious public relations, legal, and ethical consequences. As the people that build these systems, we have a social responsibility to consider their effect on humanity, and we should do whatever we can to prevent these models from perpetuating some of the prejudice and bias that exist in our society today. In this talk I intend to cover some examples of recommendation systems that have gone wrong across various industries, as well as why they went wrong and what can be done about it. The first step towards solving this larger issue is raising awareness, but there are concrete technical approaches that can be employed as well. Three that will be covered are: Accepting simplicity with interpretable models. Data segregation via ensemble modelling. Designing test data sets for capturing unintended bias.