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    <abstract>Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all test-takers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.</abstract>
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%0 Conference Proceedings
%T Building Better Open-Source Tools to Support Fairness in Automated Scoring
%A Madnani, Nitin
%A Loukina, Anastassia
%A von Davier, Alina
%A Burstein, Jill
%A Cahill, Aoife
%Y Hovy, Dirk
%Y Spruit, Shannon
%Y Mitchell, Margaret
%Y Bender, Emily M.
%Y Strube, Michael
%Y Wallach, Hanna
%S Proceedings of the First ACL Workshop on Ethics in Natural Language Processing
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F madnani-etal-2017-building
%X Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all test-takers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.
%R 10.18653/v1/W17-1605
%U https://aclanthology.org/W17-1605
%U https://doi.org/10.18653/v1/W17-1605
%P 41-52

Markdown (Informal)

[Building Better Open-Source Tools to Support Fairness in Automated Scoring](https://aclanthology.org/W17-1605) (Madnani et al., EthNLP 2017)

• Building Better Open-Source Tools to Support Fairness in Automated Scoring (Madnani et al., EthNLP 2017)

ACL

• Nitin Madnani, Anastassia Loukina, Alina von Davier, Jill Burstein, and Aoife Cahill. 2017. Building Better Open-Source Tools to Support Fairness in Automated Scoring. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, pages 41–52, Valencia, Spain. Association for Computational Linguistics.