@conference {12406,
	title = {Modeling diverse standpoints in text classification: learning to be human by modeling human values},
	booktitle = {Proceedings of the 2011 iConference},
	series = {iConference {\textquoteright}11},
	year = {2011},
	month = {2011///},
	pages = {672 - 673},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {An annotator{\textquoteright}s classification of a text not only tells us something about the intent of the text{\textquoteright}s author, it also tells us something about the annotator{\textquoteright}s standpoint. To understand authorial intent, we can consider all of these diverse standpoints, as well as the extent to which the annotators{\textquoteright} standpoints affect their perceptions of authorial intent. To model human behavior, it is important to model humans{\textquoteright} unique standpoints. Human values play an especially important role in determining human behavior and how people perceive the world around them, so any effort to model human behavior and perception can benefit from an effort to understand and model human values. Instead of training humans to obscure their standpoints and act like computers, we should teach computers to have standpoints of their own.},
	keywords = {diversity, framing theory, machine learning, standpoint epistemology, value sensitive computing},
	isbn = {978-1-4503-0121-3},
	doi = {10.1145/1940761.1940863},
	url = {http://doi.acm.org/10.1145/1940761.1940863},
	author = {Fleischmann,Kenneth R. and Templeton,Thomas Clay and Jordan Boyd-Graber}
}
