research-article

“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI

Authors:

Nithya Sambasivan,

Shivani Kapania,

Hannah Highfill,

Praveen Paritosh,

Lora M AroyoAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 39, Pages 1 - 15

https://doi.org/10.1145/3411764.3445518

Published: 07 May 2021 Publication History

Abstract

AI models are increasingly applied in high-stakes domains like health and conservation. Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations. Paradoxically, data is the most under-valued and de-glamorised aspect of AI. In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA. We define, identify, and present empirical evidence on Data Cascades—compounding events causing negative, downstream effects from data issues—triggered by conventional AI/ML practices that undervalue data quality. Data cascades are pervasive (92% prevalence), invisible, delayed, but often avoidable. We discuss HCI opportunities in designing and incentivizing data excellence as a first-class citizen of AI, resulting in safer and more robust systems for all.

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Cited By

Sundberg LHolmstrom J(2024)Teaching Tip Using No-Code AI to Teach Machine Learning in Higher EducationJournal of Information Systems Education10.62273/CYPL2902(56-66)Online publication date: Jan-2024
https://doi.org/10.62273/CYPL2902
Bender ASouza EBender ICorrêa UAraujo R(2024)Integrating Domain Knowledge in Multi-Source Classification TasksJournal on Interactive Systems10.5753/jis.2024.409615:1(591-614)Online publication date: 29-Jun-2024
https://doi.org/10.5753/jis.2024.4096
Oluka A(2024)Mitigating Biases in Training Data: Technical and Legal Challenges for Sub-Saharan AfricaInternational Journal of Applied Research in Business and Management10.51137/ijarbm.2024.5.1.105:1(209-224)Online publication date: 24-May-2024
https://doi.org/10.51137/ijarbm.2024.5.1.10
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Index Terms

“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
1. Applied computing
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

General Chairs:
Yoshifumi Kitamura
Tohoku University, Japan
,
Aaron Quigley
University of New South Wales, Australia
,
Program Chairs:
Katherine Isbister
University of California Santa Cruz, USA
,
Takeo Igarashi
The University of Tokyo, Japan
,
Publications Chairs:
Pernille Bjørn
University of Copenhagen, Denmark
,
Steven Drucker
Microsoft Research, USA

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Published: 07 May 2021

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CHI '21: CHI Conference on Human Factors in Computing Systems

May 8 - 13, 2021

Yokohama, Japan

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Sundberg LHolmstrom J(2024)Teaching Tip Using No-Code AI to Teach Machine Learning in Higher EducationJournal of Information Systems Education10.62273/CYPL2902(56-66)Online publication date: Jan-2024
https://doi.org/10.62273/CYPL2902
Bender ASouza EBender ICorrêa UAraujo R(2024)Integrating Domain Knowledge in Multi-Source Classification TasksJournal on Interactive Systems10.5753/jis.2024.409615:1(591-614)Online publication date: 29-Jun-2024
https://doi.org/10.5753/jis.2024.4096
Oluka A(2024)Mitigating Biases in Training Data: Technical and Legal Challenges for Sub-Saharan AfricaInternational Journal of Applied Research in Business and Management10.51137/ijarbm.2024.5.1.105:1(209-224)Online publication date: 24-May-2024
https://doi.org/10.51137/ijarbm.2024.5.1.10
Muchenje CMtengwa EMaregere L(2024)Unleashing Entrepreneurial PotentialAI-Driven Marketing Research and Data Analytics10.4018/979-8-3693-2165-2.ch021(378-405)Online publication date: 19-Apr-2024
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Meiser MZinnikus I(2024)A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and OpportunitiesEnergies10.3390/en1709199217:9(1992)Online publication date: 23-Apr-2024
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Narushynska OTeslyuk VDoroshenko AArzubov M(2024)Data Sorting Influence on Short Text Manual Labeling Quality for Hierarchical ClassificationBig Data and Cognitive Computing10.3390/bdcc80400418:4(41)Online publication date: 7-Apr-2024
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Bolívar AGarcía VAlejo RFlorencia-Juárez RSánchez J(2024)Data-Centric Solutions for Addressing Big Data Veracity with Class Imbalance, High Dimensionality, and Class OverlappingApplied Sciences10.3390/app1413584514:13(5845)Online publication date: 4-Jul-2024
https://doi.org/10.3390/app14135845
Ragab MAdidarma ATommasini R(2024)PAPAYA: A library for performance analysis of SQL-based RDF processing systemsSemantic Web10.3233/SW-243582(1-19)Online publication date: 5-Apr-2024
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Balagopalan ABaldini ICeli LGichoya JMcCoy LNaumann TShalit Uvan der Schaar MWagstaff K(2024)Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impactPLOS Digital Health10.1371/journal.pdig.00004743:4(e0000474)Online publication date: 15-Apr-2024
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Doran SBarfoot TWedlake LWinfield JPetts JGlocker BLi XLeach MKaiser MBarwick TChaidos ASatchwell LSoneji NElgendy KSheeka AWallitt KKoh DMessiou CRockall A(2024)Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world dataInsights into Imaging10.1186/s13244-023-01591-715:1Online publication date: 16-Feb-2024
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