3940414243444542 of 204
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Probing User Perceptions on Machine Learning
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Att Sondera Användares Förståelse av Maskininlärning (Swedish)
Abstract [en]

Machine Learning is a technology that has risen in popularity in the last decade. Designers face difficulties in working with Machine Learning as a design material. In order to help designers to cope with this material, many different approaches have been suggested, from books to insights of experienced designers with Machine Learning. In this research, the focus is on the users’ perceptions on Machine Learning and how these could contribute to better design. For this purpose, 10 participants deployed probes to investigate the term Machine Learning. Probes consisted of simple tasks that provoked participants to recognize Machine Learning elements in applications they already use and were deployed with the use of their smart phones. Participants formed personalized perceptions on Machine Learning which varied from creativity in Machine Learning to preoccupations about data use. Based on these findings, suggestions to designers were proposed. Moreover, a secondary research question that emerged was the difficulties the researcher faced while working with probing on Machine Learning user experiences for the specific research.

Abstract [sv]

Maskininlärning är ett teknologi som har blivit populär det senaste decenniet. Som designer kan det vara svårt att jobba med maskininlärning som ett “designmaterial". Olika tillvägagångssätt har föreslagits för att hjälpa designers att hantera det här material. I studien som presenteras

här läggs fokus på användarens uppfattningar om maskininlärning och hur deras förståelse skulle kunna bidra till bättre design. Tio deltagare använde så kallade “probes" i syfte att undersöka hur vi möter maskininlärning i vardagen. Dessa “probes" bestod av enkla uppgifter som uppmuntrade deltagare att notera och utforska hur maskininlärning ingår som element i tillämpningar som de använder i t ex smartphones. Deltagarna uttryckte sin personliga förståelse och funderingar om maskininlärning, vilket omfattade allt från kreativitet till oro kring hur personliga data används i dessa system. Baserat på en analys av resultaten formulerar vi råd till hur en designer ska utforma interaktion med maskininlärningssystem. Slutligen adderar vi en reflektion om svårigheterna med att använda probes för att studera maskininlärning.

Place, publisher, year, edition, pages
2019. , p. 15
Series
TRITA-EECS-EX ; 2019:821
Keywords [en]
Probes; Machine Learning; User Experience
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-271200OAI: oai:DiVA.org:kth-271200DiVA, id: diva2:1415986
Subject / course
Computer Science
Educational program
Master of Science - Human-Computer Interaction
Supervisors
Examiners
Available from: 2020-03-20 Created: 2020-03-20 Last updated: 2020-03-20Bibliographically approved

Open Access in DiVA

fulltext(805 kB)2 downloads
File information
File name FULLTEXT01.pdfFile size 805 kBChecksum SHA-512
645e5d3a2258dcff27bfc0e407d9cd8283f5f83ce1e15f5125d92bd39fb4ba00c10aa56656fef1003ba83966912ca73dc9ae2b4dc0b5f6c23f7f8c77d6723102
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering and Computer Science (EECS)
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 2 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 13 hits
3940414243444542 of 204
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf