Quantifying Transparency of Machine Learning Systems through Analysis of Contributions

8 July 2019

Papers citing "Quantifying Transparency of Machine Learning Systems through Analysis of Contributions"

9 / 9 papers shown

Title
A Conceptual Architecture for Contractual Data Sharing in a Decentralised Environment I. Barclay Alun D. Preece Ian J. Taylor D. Verma 18 7 0 05 Apr 2019
Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform Shuai Zhao Manoop Talasila G. Jacobson Cristian Borcea Syed Anwar Aftab John F. Murray 23 33 0 16 Oct 2018
Model Cards for Model Reporting Margaret Mitchell Simone Wu Andrew Zaldivar Parker Barnes Lucy Vasserman Ben Hutchinson Elena Spitzer Inioluwa Deborah Raji Timnit Gebru 125 1,895 0 05 Oct 2018
Stakeholders in Explainable AI Alun D. Preece Daniel Harborne Dave Braines Richard J. Tomsett Supriyo Chakraborty 40 156 0 29 Sep 2018
Defining the Collective Intelligence Supply Chain I. Barclay Alun D. Preece Ian J. Taylor 26 4 0 25 Sep 2018
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity Matthew Arnold Rachel K. E. Bellamy Michael Hind Stephanie Houde S. Mehta ... Darrell Reimer Alexandra Olteanu David Piorkowski Jason Tsay Kush R. Varshney HILM 47 137 0 22 Aug 2018
A Survey on Essential Components of a Self-Sovereign Identity Alexander Mühle Andreas Grüner T. Gayvoronskaya Christoph Meinel 43 355 0 17 Jul 2018
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 122 1,772 0 22 Aug 2017
Defending against Sybil Devices in Crowdsourced Mapping Services Gang Wang Bolun Wang Tianyi Wang A. Nika Haitao Zheng Ben Y. Zhao 50 97 0 04 Aug 2015