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"What We Can't Measure, We Can't Understand": Challenges to Demographic
  Data Procurement in the Pursuit of Fairness
v1v2 (latest)

"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness

Conference on Fairness, Accountability and Transparency (FAccT), 2020
30 October 2020
Mckane Andrus
Elena Spitzer
Jeffrey Brown
Alice Xiang
ArXiv (abs)PDFHTML

Papers citing ""What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness"

50 / 59 papers shown
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Demographic-Agnostic Fairness without Harm
Demographic-Agnostic Fairness without Harm
Zhongteng Cai
Mohammad Mahdi Khalili
Xueru Zhang
121
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28 Sep 2025
Beyond Internal Data: Bounding and Estimating Fairness from Incomplete Data
Beyond Internal Data: Bounding and Estimating Fairness from Incomplete Data
Varsha Ramineni
Hossein A. Rahmani
Emine Yilmaz
David Barber
125
0
0
18 Aug 2025
Beyond Internal Data: Constructing Complete Datasets for Fairness Testing
Beyond Internal Data: Constructing Complete Datasets for Fairness Testing
Varsha Ramineni
Hossein A. Rahmani
Emine Yilmaz
David Barber
139
0
0
24 Jul 2025
Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems
Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems
Jing Nathan Yan
Emma Harvey
Junxiong Wang
Jeffrey M. Rzeszotarski
Allison Koenecke
FaML
304
0
0
26 May 2025
Talking About the Assumption in the Room
Talking About the Assumption in the RoomInternational Conference on Human Factors in Computing Systems (CHI), 2025
Ramaravind Kommiya Mothilal
Faisal M. Lalani
Syed Ishtiaque Ahmed
Shion Guha
Sharifa Sultana
286
2
0
20 Feb 2025
Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool
Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool
Floris Holstege
Mackenzie Jorgensen
Kirtan Padh
Jurriaan Parie
Joel Persson
Krsto Prorokovic
Lukas Snoek
392
1
0
03 Feb 2025
Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree
Accurate and Data-Efficient Toxicity Prediction when Annotators DisagreeConference on Empirical Methods in Natural Language Processing (EMNLP), 2024
Harbani Jaggi
Kashyap Murali
Eve Fleisig
Erdem Bıyık
153
3
0
16 Oct 2024
Minimum Viable Ethics: From Institutionalizing Industry AI Governance to
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Minimum Viable Ethics: From Institutionalizing Industry AI Governance to Product Impact
Archana Ahlawat
Amy Winecoff
Jonathan Mayer
220
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11 Sep 2024
Position: Measure Dataset Diversity, Don't Just Claim It
Position: Measure Dataset Diversity, Don't Just Claim It
Dora Zhao
Jerone T. A. Andrews
Orestis Papakyriakopoulos
Alice Xiang
275
31
0
11 Jul 2024
FairJob: A Real-World Dataset for Fairness in Online Systems
FairJob: A Real-World Dataset for Fairness in Online Systems
Mariia Vladimirova
Federico Pavone
Eustache Diemert
320
6
0
03 Jul 2024
A Taxonomy of Challenges to Curating Fair Datasets
A Taxonomy of Challenges to Curating Fair Datasets
Dora Zhao
M. Scheuerman
Pooja Chitre
Jerone T. A. Andrews
Georgia Panagiotidou
Shawn Walker
Kathleen H. Pine
Alice Xiang
277
4
0
10 Jun 2024
Addressing Discretization-Induced Bias in Demographic Prediction
Addressing Discretization-Induced Bias in Demographic Prediction
Evan Dong
Aaron Schein
Yixin Wang
Nikhil Garg
219
6
0
27 May 2024
Interpretability Needs a New Paradigm
Interpretability Needs a New Paradigm
Andreas Madsen
Himabindu Lakkaraju
Siva Reddy
Sarath Chandar
201
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0
08 May 2024
Lazy Data Practices Harm Fairness Research
Lazy Data Practices Harm Fairness Research
Jan Simson
Alessandro Fabris
Christoph Kern
198
10
0
26 Apr 2024
Fairness Without Harm: An Influence-Guided Active Sampling Approach
Fairness Without Harm: An Influence-Guided Active Sampling Approach
Jinlong Pang
Jialu Wang
Zhaowei Zhu
Yuanshun Yao
Chen Qian
Yang Liu
TDI
264
8
0
20 Feb 2024
AboutMe: Using Self-Descriptions in Webpages to Document the Effects of
  English Pretraining Data Filters
AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data FiltersAnnual Meeting of the Association for Computational Linguistics (ACL), 2024
L. Lucy
Suchin Gururangan
Luca Soldaini
Emma Strubell
David Bamman
Lauren Klein
Jesse Dodge
346
18
0
12 Jan 2024
Modeling subjectivity (by Mimicking Annotator Annotation) in toxic
  comment identification across diverse communities
Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities
Senjuti Dutta
Sid Mittal
Sherol Chen
Deepak Ramachandran
Ravi Rajakumar
Ian D Kivlichan
Sunny Mak
Alena Butryna
Praveen Paritosh University of Tennessee
276
8
0
01 Nov 2023
"One-Size-Fits-All"? Examining Expectations around What Constitute
  "Fair" or "Good" NLG System Behaviors
"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System BehaviorsNorth American Chapter of the Association for Computational Linguistics (NAACL), 2023
Li Lucy
Su Lin Blodgett
Milad Shokouhi
Hanna M. Wallach
Alexandra Olteanu
302
12
0
23 Oct 2023
Estimating and Implementing Conventional Fairness Metrics With
  Probabilistic Protected Features
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features
Hadi Elzayn
Emily Black
Patrick Vossler
Nathanael Jo
Jacob Goldin
Daniel E. Ho
142
7
0
02 Oct 2023
Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color
Beyond Skin Tone: A Multidimensional Measure of Apparent Skin ColorIEEE International Conference on Computer Vision (ICCV), 2023
William Thong
Przemyslaw K. Joniak
Alice Xiang
284
26
0
10 Sep 2023
TIDE: Textual Identity Detection for Evaluating and Augmenting
  Classification and Language Models
TIDE: Textual Identity Detection for Evaluating and Augmenting Classification and Language Models
Emmanuel Klu
Sameer Sethi
190
0
0
07 Sep 2023
When Fair Classification Meets Noisy Protected Attributes
When Fair Classification Meets Noisy Protected AttributesAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2023
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Pablo Kvitca
Chris L. Wilson
FaML
172
10
0
06 Jul 2023
Evaluating the Social Impact of Generative AI Systems in Systems and
  Society
Evaluating the Social Impact of Generative AI Systems in Systems and Society
Irene Solaiman
Zeerak Talat
William Agnew
Lama Ahmad
Dylan K. Baker
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Marie-Therese Png
Shubham Singh
A. Strait
Lukas Struppek
Arjun Subramonian
ELMEGVM
481
147
0
09 Jun 2023
Disentangling and Operationalizing AI Fairness at LinkedIn
Disentangling and Operationalizing AI Fairness at LinkedInConference on Fairness, Accountability and Transparency (FAccT), 2023
Joaquin Quiñonero Candela
Yuwen Wu
Brian Hsu
Sakshi Jain
Jennifer Ramos
Jon Adams
R. Hallman
Kinjal Basu
220
11
0
30 May 2023
A View From Somewhere: Human-Centric Face Representations
A View From Somewhere: Human-Centric Face RepresentationsInternational Conference on Learning Representations (ICLR), 2023
Jerone T. A. Andrews
Przemyslaw K. Joniak
Alice Xiang
CVBM
176
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0
30 Mar 2023
Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?
Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?Conference on Fairness, Accountability and Transparency (FAccT), 2023
Shreyans Chowdhary
Anna Kawakami
Mary L. Gray
J. Suh
Alexandra Olteanu
Koustuv Saha
250
42
0
13 Mar 2023
Overwriting Pretrained Bias with Finetuning Data
Overwriting Pretrained Bias with Finetuning DataIEEE International Conference on Computer Vision (ICCV), 2023
Angelina Wang
Olga Russakovsky
315
46
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10 Mar 2023
Multi-dimensional discrimination in Law and Machine Learning -- A
  comparative overview
Multi-dimensional discrimination in Law and Machine Learning -- A comparative overviewConference on Fairness, Accountability and Transparency (FAccT), 2023
Arjun Roy
J. Horstmann
Eirini Ntoutsi
FaML
165
27
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12 Feb 2023
Ethical Considerations for Responsible Data Curation
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Jerone T. A. Andrews
Dora Zhao
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Apostolos Modas
Orestis Papakyriakopoulos
Alice Xiang
418
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0
07 Feb 2023
An Operational Perspective to Fairness Interventions: Where and How to
  Intervene
An Operational Perspective to Fairness Interventions: Where and How to Intervene
Brian Hsu
Xiaotong Chen
Ying Han
Hongseok Namkoong
Kinjal Basu
305
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03 Feb 2023
Simplicity Bias Leads to Amplified Performance Disparities
Simplicity Bias Leads to Amplified Performance DisparitiesConference on Fairness, Accountability and Transparency (FAccT), 2022
Samuel J. Bell
Levent Sagun
214
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Fair Ranking with Noisy Protected Attributes
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A Keyword Based Approach to Understanding the Overpenalization of
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Weak Proxies are Sufficient and Preferable for Fairness with Missing
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Representing Marginalized Populations: Challenges in Anthropographics
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Exploring How Machine Learning Practitioners (Try To) Use Fairness
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Subverting Fair Image Search with Generative Adversarial Perturbations
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