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Using Language Models to Simulate Human Samples: Acknowledgments and Referencesby@textmodels
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Using Language Models to Simulate Human Samples: Acknowledgments and References

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Datasheets for datasets have gained traction across academic and industry settings, fostering transparency and accountability. While implementation challenges exist, the benefits of improved communication and accountability outweigh the costs, driving adoption and evolution in dataset creation practices.
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Authors:

(1) TIMNIT GEBRU, Black in AI;

(2) JAMIE MORGENSTERN, University of Washington;

(3) BRIANA VECCHIONE, Cornell University;

(4) JENNIFER WORTMAN VAUGHAN, Microsoft Research;

(5) HANNA WALLACH, Microsoft Research;

(6) HAL DAUMÉ III, Microsoft Research; University of Maryland;

(7) KATE CRAWFORD, Microsoft Research.

1 Introduction

1.1 Objectives

2 Development Process

3 Questions and Workflow

3.1 Motivation

3.2 Composition

3.3 Collection Process

3.4 Preprocessing/cleaning/labeling

3.5 Uses

3.6 Distribution

3.7 Maintenance

4 Impact and Challenges

Acknowledgments and References

Appendix

Acknowledgments

We thank Peter Bailey, Emily Bender, Yoshua Bengio, Sarah Bird, Sarah Brown, Steven Bowles, Joy Buolamwini, Amanda Casari, Eric Charran, Alain Couillault, Lukas Dauterman, Leigh Dodds, Miroslav Dudík, Michael Ekstrand, Noémie Elhadad, Michael Golebiewski, Nick Gonsalves, Martin Hansen, Andy Hickl, Michael Hoffman, Scott Hoogerwerf, Eric Horvitz, Mingjing Huang, Surya Kallumadi, Ece Kamar, Krishnaram Kenthapadi, Emre Kiciman, Jacquelyn Krones, Erik Learned-Miller, Lillian Lee, Jochen Leidner, Rob Mauceri, Brian Mcfee, Emily McReynolds, Bogdan Micu, Margaret Mitchell, Sangeeta Mudnal, Brendan O’Connor, Thomas Padilla, Bo Pang, Anjali Parikh, Lisa Peets, Alessandro Perina, Michael Philips, Barton Place, Sudha Rao, Jen Ren, David Van Riper, Anna Roth, Cynthia Rudin, Ben Shneiderman, Biplav Srivastava, Ankur Teredesai, Rachel Thomas, Martin Tomko, Panagiotis Tziachris, Meredith Whittaker, Hans Wolters, Ashly Yeo, Lu Zhang, and the attendees of the Partnership on AI’s April 2019 ABOUT ML workshop for valuable feedback.

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This paper is available on arxiv under CC 4.0 license.