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ABOUT ML: Annotation and Benchmarking on Understanding and Transparency
  of Machine Learning Lifecycles
v1v2v3 (latest)

ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles

12 December 2019
Inioluwa Deborah Raji
Jingyi Yang
ArXiv (abs)PDFHTML

Papers citing "ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles"

22 / 22 papers shown
Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement
Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement
Minja Axelsson
Jiaee Cheong
Rune Nyrup
Hatice Gunes
224
3
0
01 Sep 2025
Developing a Risk Identification Framework for Foundation Model Uses
Developing a Risk Identification Framework for Foundation Model Uses
David Piorkowski
Michael Hind
John T. Richards
Jacquelyn Martino
144
1
0
01 Jun 2025
Toward an Evaluation Science for Generative AI Systems
Toward an Evaluation Science for Generative AI Systems
Laura Weidinger
Deb Raji
Hanna M. Wallach
Margaret Mitchell
Angelina Wang
Olawale Salaudeen
Rishi Bommasani
Sayash Kapoor
Deep Ganguli
Sanmi Koyejo
EGVMELM
453
37
0
07 Mar 2025
Improving governance outcomes through AI documentation: Bridging theory
  and practice
Improving governance outcomes through AI documentation: Bridging theory and practiceInternational Conference on Human Factors in Computing Systems (CHI), 2024
Amy A. Winecoff
Miranda Bogen
278
16
0
13 Sep 2024
Leveraging Ontologies to Document Bias in Data
Leveraging Ontologies to Document Bias in Data
Mayra Russo
Maria-Esther Vidal
267
2
0
29 Jun 2024
What's documented in AI? Systematic Analysis of 32K AI Model Cards
What's documented in AI? Systematic Analysis of 32K AI Model Cards
Weixin Liang
Nazneen Rajani
Xinyu Yang
Ezinwanne Ozoani
Eric Wu
Yiqun Chen
D. Smith
James Zou
242
26
0
07 Feb 2024
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A
  Case Study
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
Hala Abdelkader
Mohamed Abdelrazek
Scott Barnett
Jean-Guy Schneider
Priya Rani
Rajesh Vasa
291
7
0
12 Jan 2024
The Fallacy of AI Functionality
The Fallacy of AI FunctionalityConference on Fairness, Accountability and Transparency (FAccT), 2022
Inioluwa Deborah Raji
Indra Elizabeth Kumar
Aaron Horowitz
Andrew D. Selbst
279
253
0
20 Jun 2022
Prescriptive and Descriptive Approaches to Machine-Learning Transparency
Prescriptive and Descriptive Approaches to Machine-Learning Transparency
David Adkins
B. Alsallakh
Adeel Cheema
Narine Kokhlikyan
Emily McReynolds
Pushkar Mishra
Chavez Procope
Jeremy Sawruk
Erin Wang
Polina Zvyagina
170
14
0
27 Apr 2022
Investigating Explainability of Generative AI for Code through
  Scenario-based Design
Investigating Explainability of Generative AI for Code through Scenario-based DesignInternational Conference on Intelligent User Interfaces (IUI), 2022
Jiao Sun
Q. V. Liao
Michael J. Muller
Mayank Agarwal
Stephanie Houde
Kartik Talamadupula
Justin D. Weisz
242
226
0
10 Feb 2022
Evaluating a Methodology for Increasing AI Transparency: A Case Study
Evaluating a Methodology for Increasing AI Transparency: A Case Study
David Piorkowski
John T. Richards
Michael Hind
242
6
0
24 Jan 2022
The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate
  Harms in Artificial Intelligence
The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence
Kasia Chmielinski
S. Newman
Matt Taylor
Joshua Joseph
Kemi Thomas
Jessica Yurkofsky
Yue Qiu
282
73
0
10 Jan 2022
Validation and Transparency in AI systems for pharmacovigilance: a case
  study applied to the medical literature monitoring of adverse events
Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse events
Bruno Ohana
Jack D. Sullivan
Nicole L. Baker
132
2
0
21 Dec 2021
Trustworthy AI: From Principles to Practices
Trustworthy AI: From Principles to Practices
Yue Liu
Peng Qi
Bo Liu
Shuai Di
Jingen Liu
Jiquan Pei
Jinfeng Yi
Bowen Zhou
571
582
0
04 Oct 2021
"Garbage In, Garbage Out" Revisited: What Do Machine Learning
  Application Papers Report About Human-Labeled Training Data?
"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?
R. Geiger
Dominique Cope
Jamie Ip
Marsha Lotosh
Aayush Shah
Jenny Weng
Rebekah Tang
195
74
0
05 Jul 2021
Data and its (dis)contents: A survey of dataset development and use in
  machine learning research
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada
Inioluwa Deborah Raji
Emily M. Bender
Emily L. Denton
A. Hanna
399
631
0
09 Dec 2020
Towards evaluating and eliciting high-quality documentation for
  intelligent systems
Towards evaluating and eliciting high-quality documentation for intelligent systems
David Piorkowski
D. González
John T. Richards
Stephanie Houde
264
12
0
17 Nov 2020
Uncertainty as a Form of Transparency: Measuring, Communicating, and
  Using Uncertainty
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using UncertaintyAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020
Umang Bhatt
Javier Antorán
Yunfeng Zhang
Q. V. Liao
P. Sattigeri
...
L. Nachman
R. Chunara
Madhulika Srikumar
Adrian Weller
Alice Xiang
408
310
0
15 Nov 2020
Prune Responsibly
Prune Responsibly
Michela Paganini
VLM
233
23
0
10 Sep 2020
Machine Learning Explainability for External Stakeholders
Machine Learning Explainability for External Stakeholders
Umang Bhatt
Mckane Andrus
Adrian Weller
Alice Xiang
FaMLSILM
213
64
0
10 Jul 2020
A Methodology for Creating AI FactSheets
A Methodology for Creating AI FactSheets
John T. Richards
David Piorkowski
Michael Hind
Stephanie Houde
Aleksandra Mojsilović
260
55
0
24 Jun 2020
Garbage In, Garbage Out? Do Machine Learning Application Papers in
  Social Computing Report Where Human-Labeled Training Data Comes From?
Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?
R. Geiger
Kevin Yu
Yanlai Yang
Mindy Dai
Jie Qiu
Rebekah Tang
Jenny Huang
198
172
0
17 Dec 2019
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