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Key Tension Points of creative Machine Learning applications keeping a Human-in-the-Loop
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 thesis
Abstract [en]

Machine learning (ML) and artificial intelligence (AI), might have earlier primarily found industrial use, improving production chains, efficiency and the like but are now an integral part of private and commercial application. Many systems are using, or are claiming to use, machine learning to improve the end user's experience.

This study aims to explore applications that are using creative ML, in which output might have a plethora of solutions instead of a single correct one. More specifically the focus is to evaluate which Key Tension Points, central lesser components of a complex and bigger issue, arise for researchers, designers and users coming into contact with this technology.

The goal is to draw upon these Key Tension Points and attempt to frame guidelines which researchers and designers can use to further their understanding of the relationship between ML and design and how they can be accounted for to build and develop better application. The study found that these tension points (Impersonality, passive consumers & transparency) vary considerably depending on the application and presents how designers can account for them.

Abstract [sv]

Maskininlärning (ML) och artificiell intelligens (AI) har sedan tidigare oftast använts på en industriell skala, för att effektivisera produktionskedjor eller förfina dessa. Dock har det skett ett skifte och nuförtiden är ML en betydande del i applikationer som har privatpersoner som målgrupp.

Den här studien undersöker kreativa maskininlärningslösningar, sådana som kommer med fler än ett förslag. Mer specifikt så undersöker den här studien vilka Key Tension Points, dvs. betydande mindre komponenter av komplexa stora problem, som forskare, designers eller användare kommer i kontakt med.

Målet är att ta fram Key Tension Points och sedan undersöka huruvida riktlinjer kan formuleras som underlättar för forskare och designers att hantera frågor rörande design och tillgänglighet av ML. Dessutom underlättar användandet av Key Tension Points vid byggandet och utvecklingen av kreativa ML applikationer. Studien fann att Key Tension Points varierar betydande beroende på vilken typ av applikation som används av konsumenten.

Place, publisher, year, edition, pages
2019. , p. 12
Series
TRITA-EECS-EX ; 2019:619
Keywords [en]
AI; Machine Learning; Key Tension Points, UX Research
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:kth:diva-264570OAI: oai:DiVA.org:kth-264570DiVA, id: diva2:1374330
Educational program
Master of Science in Engineering - Media Technology
Supervisors
Examiners
Available from: 2019-11-29 Created: 2019-11-29

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