Abstract: As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs.
Authors: Finale Doshi-Velez, Been Kim
Abstract: The blind application of machine learning runs the risk of amplifying biases presentin data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks...
Authors: Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai
Abstract: A growing body of work shows that many problems in fairness,accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process.
Authors: Eun Seo Jo, Timnit Gebru