Data and algorithmic bias in the web

The Web is the largest public big data repository that humankind has created. In this overwhelming data ocean we need to be aware of the quality of data extracted from it. One important quality issue is data bias, which appears in different forms. These biases affect the (machine learning) algorithms that we design to improve the user experience. This problem is further exacerbated by biases that are added by these algorithms, especially in the context of recommendation and personalization systems. We give several examples, stressing the importance of the user context to avoid these biases.