J
Jennifer Wortman Vaughan
Researcher at Microsoft
Publications - 101
Citations - 9120
Jennifer Wortman Vaughan is an academic researcher from Microsoft. The author has contributed to research in topics: Crowdsourcing & Market maker. The author has an hindex of 29, co-authored 96 publications receiving 5715 citations. Previous affiliations of Jennifer Wortman Vaughan include University of California, Los Angeles & University of Pennsylvania.
Papers
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Journal ArticleDOI
A theory of learning from different domains
Shai Ben-David,John Blitzer,Koby Crammer,Alex Kulesza,Fernando Pereira,Jennifer Wortman Vaughan +5 more
TL;DR: A classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains and shows how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class.
Posted Content
Datasheets for Datasets
Timnit Gebru,Jamie Morgenstern,Briana Vecchione,Jennifer Wortman Vaughan,Hanna Wallach,Hal Daumé,Kate Crawford +6 more
TL;DR: Documentation to facilitate communication between dataset creators and consumers and consumers is presented.
Posted Content
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh,Daniel G. Goldstein,Jake M. Hofman,Jennifer Wortman Vaughan,Hanna Wallach +4 more
TL;DR: A sequence of pre-registered experiments showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box).
Proceedings ArticleDOI
Improving fairness in machine learning systems: What do industry practitioners need?
TL;DR: In this article, the authors conduct a systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems and identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature.
Proceedings Article
Online task assignment in crowdsourcing markets
TL;DR: This work presents a two-phase exploration-exploitation assignment algorithm and proves that it is competitive with respect to the optimal offline algorithm which has access to the unknown skill levels of each worker.