Data and Algorithmic Bias in the Web
published: Oct. 6, 2016, recorded: September 2016, views: 20
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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 and, in particular, of the biases that exist in this data. In the Web, biases also come from redundancy and spam, as well as from algorithms that we design to improve the user experience. This problem is further exacerbated by biases that are added by these algorithms, specially in the context of search and recommendation systems. They include selection and presentation bias in many forms, interaction bias, social bias, etc. We give several examples and their relation to sparsity and privacy, stressing the importance of the user context to avoid these biases.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !