Margaret Mitchell (scientist)

Margaret Mitchell
Margaret Mitchell
	Mitchell (2022)
Born	United States
Other names	Shmargaret Shmitchell
Alma mater	University of Aberdeen (PhD in Computer Science) ; University of Washington (MSc in Computational Linguistics)
Known for	Algorithmic bias ; Fairness in Machine Learning ; Computer Vision ; Natural Language Processing
	Scientific career
Fields	Computer Science
Institutions	Google ; Microsoft Research ; Johns Hopkins University
Thesis	Generating Reference to Visible Objects (2012)
Website	Personal website

Margaret Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning. She is most well known for her work on automatically removing undesired biases concerning demographic groups from machine learning models,^[2] as well as more transparent reporting of their intended use.^[3]

^ Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret (2021-03-01). "On the Dangers of Stochastic Parrots". Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT '21. New York, NY, USA: Association for Computing Machinery. pp. 610–623. doi:10.1145/3442188.3445922. ISBN 978-1-4503-8309-7. S2CID 232040593.
^ Hu Zhang, Brian; Lemoine, Blake; Mitchell, Margaret (2018-12-01). "Mitigating Unwanted Biases with Adversarial Learning". Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. AAAI/ACM Conference on AI, Ethics, and Society. pp. 220–229. arXiv:1801.07593. doi:10.1145/3278721.3278779.
^ Mitchell, Margaret; Wu, Simone; Zaldivar, Andrew; Barnes, Parker; Vasserman, Lucy; Hutchinson, Ben; Spitzer, Elena; Raji, Inioluwa Deborah; Gebru, Timnit (2019-01-29). "Model Cards for Model Reporting". Proceedings of the Conference on Fairness, Accountability, and Transparency. Conference on Fairness, Accountability, and Transparency. arXiv:1810.03993. doi:10.1145/3287560.3287596.

[parrot-paper-1] Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret (2021-03-01). "On the Dangers of Stochastic Parrots". Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT '21. New York, NY, USA: Association for Computing Machinery. pp. 610–623. doi:10.1145/3442188.3445922. ISBN 978-1-4503-8309-7. S2CID 232040593.

[adversarial_removal-2] Hu Zhang, Brian; Lemoine, Blake; Mitchell, Margaret (2018-12-01). "Mitigating Unwanted Biases with Adversarial Learning". Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. AAAI/ACM Conference on AI, Ethics, and Society. pp. 220–229. arXiv:1801.07593. doi:10.1145/3278721.3278779.

[model_cards-3] Mitchell, Margaret; Wu, Simone; Zaldivar, Andrew; Barnes, Parker; Vasserman, Lucy; Hutchinson, Ben; Spitzer, Elena; Raji, Inioluwa Deborah; Gebru, Timnit (2019-01-29). "Model Cards for Model Reporting". Proceedings of the Conference on Fairness, Accountability, and Transparency. Conference on Fairness, Accountability, and Transparency. arXiv:1810.03993. doi:10.1145/3287560.3287596.

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