HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Human-Centered Machine Learning

Marco Gillies 1 Rebecca Fiebrink 1 Atau Tanaka 1 Baptiste Caramiaux 1 Jérémie Garcia 1 Frédéric Bevilacqua 2 Alexis Heloir 3, 4 Fabrizio Nunnari 3 Wendy Mackay 5, 6, 7 Saleema Amershi 8 Bongshin Lee 8 Nicolas D 'Alessandro 9 Joëlle Tilmanne 9 Todd Kulesza 10
1 Goldsmiths College - Goldsmiths, University of London
2 STMS - Sciences et Technologies de la Musique et du Son
3 DFKI - Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence
4 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201
5 EX-SITU - Extreme Interaction
LRI - Laboratoire de Recherche en Informatique, Inria Saclay - Ile de France
6 LRI - Laboratoire de Recherche en Informatique
7 Université Paris-Saclay
8 Microsoft Research [Redmond]
9 UMons - Université de Mons
10 OSU - Oregon State University
Abstract : Machine learning is one of the most important and successful techniques in contemporary computer science. It involves the statistical inference of models (such as classifiers) from data. It is often conceived in a very impersonal way, with algorithms working autonomously on passively collected data. However, this viewpoint hides considerable human work of tuning the algorithms, gathering the data, and even deciding what should be modeled in the first place. Examining machine learning from a human-centered perspective includes explicitly recognising this human work, as well as reframing machine learning workflows based on situated human working practices, and exploring the co-adaptation of humans and systems. A human-centered understanding of machine learning in human context can lead not only to more usable machine learning tools, but to new ways of framing learning computationally. This workshop will bring together researchers to discuss these issues and suggest future research questions aimed at creating a human-centered approach to machine learning.
Keywords : Machine learning User centered design Data
Document type :
Conference papers
Complete list of metadata

Cited literature [8 references]  Display  Hide  Download
https://hal.inria.fr/hal-01437057
Contributor : Wendy Mackay Connect in order to contact the contributor
Submitted on : Tuesday, January 17, 2017 - 12:47:18 AM
Last modification on : Wednesday, April 20, 2022 - 3:58:07 PM
Long-term archiving on: : Tuesday, April 18, 2017 - 12:28:45 PM

File

Vignette du document
2016 CHI Workshop HCML.pdf
Files produced by the author(s)

Identifiers

Collections

CNRS | IRCAM | UPMC | UMR8623 | INRIA | INRIA2 | LRI-HCC | CENTRALESUPELEC | UNIV-PARIS-SACLAY | STMS | SORBONNE-UNIVERSITE | SU-SCIENCES | HCI-SORBONNE | SU-TI | GS-COMPUTER-SCIENCE | LISN | LISN-EX-SITU | GS-ENGINEERING | UNIV-VALENCIENNES | LAMIH

Citation

Marco Gillies, Rebecca Fiebrink, Atau Tanaka, Baptiste Caramiaux, Jérémie Garcia, et al.. Human-Centered Machine Learning. Conference on Human Factors in Computing Systems (CHI 2016), ACM, May 2016, San Jose, California, United States. pp.3558-3565, ⟨10.1145/2851581.2856492⟩. ⟨hal-01437057⟩

Export

BibTeX TEI DC DCterms EndNote Datacite

Share

Metrics

Record views

973

Files downloads

1585