@InProceedings{schnoebelen:2017:EthNLP,
  author    = {Schnoebelen, Tyler},
  title     = {Goal-Oriented Design for Ethical Machine Learning and NLP},
  booktitle = {Proceedings of the First ACL Workshop on Ethics in Natural Language Processing},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {88--93},
  abstract  = {The argument made in this paper is that to act ethically in machine learning
	and NLP requires focusing on goals. NLP projects are often classificatory
	systems that deal with human subjects, which means that goals from people
	affected by the systems should be included. The paper takes as its core example
	a model that detects criminality, showing the problems of training data,
	categories, and outcomes. The paper is oriented to the kinds of critiques on
	power and the reproduction of inequality that are found in social theory, but
	it also includes concrete suggestions on how to put goal-oriented design into
	practice.},
  url       = {http://www.aclweb.org/anthology/W17-1611}
}