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Human In Command Machine Learning
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0001-5676-1931
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. The focus in this work is one of these areas; ML systems where decisions concerning ML model training, usage and selection of target domain lay in the hands of domain experts. 

This work is then on ML systems that function as a tool that augments and/or enhance human capabilities. The approach presented is denoted Human In Command ML (HIC-ML) systems. To enquire into this research domain design experiments of varying fidelity were used. Two of these experiments focus on augmenting human capabilities and targets the domains commuting and sorting batteries. One experiment focuses on enhancing human capabilities by identifying similar hand-painted plates. The experiments are used as illustrative examples to explore settings where domain experts potentially can: independently train an ML model and in an iterative fashion, interact with it and interpret and understand its decisions. 

HIC-ML should be seen as a governance principle that focuses on adding value and meaning to users. In this work, concrete application areas are presented and discussed. To open up for designing ML-based products for the area an abstract model for HIC-ML is constructed and design guidelines are proposed. In addition, terminology and abstractions useful when designing for explicability are presented by imposing structure and rigidity derived from scientific explanations. Together, this opens up for a contextual shift in ML and makes new application areas probable, areas that naturally couples the usage of AI technology to human virtues and potentially, as a consequence, can result in a democratisation of the usage and knowledge concerning this powerful technology.

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2021. , p. 136
Series
Studies in Computer Science ; 16
Keywords [en]
Human-centered AI/ML, Explainable AI, Machine Learning, Human In the Loop ML
National Category
Human Computer Interaction Computer Engineering
Research subject
Interaktionsdesign
Identifiers
URN: urn:nbn:se:mau:diva-42576DOI: 10.24834/isbn.9789178771875ISBN: 978-91-7877-186-8 (print)ISBN: 978-91-7877-187-5 (electronic)OAI: oai:DiVA.org:mau-42576DiVA, id: diva2:1559383
Presentation
2021-06-17, 13:00 (English)
Supervisors
Available from: 2021-06-03 Created: 2021-06-02 Last updated: 2023-07-06Bibliographically approved
List of papers
1. Contextual machine teaching
Open this publication in new window or tab >>Contextual machine teaching
2020 (English)In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2020Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning research today is dominated by atechnocentric perspective and in many cases disconnected fromthe users of the technology. The machine teaching paradigm insteadshifts the focus from machine learning experts towards thedomain experts and users of machine learning technology. Thisshift opens up for new perspectives on the current use of machinelearning as well as new usage areas to explore. In this study,we apply and map existing machine teaching principles ontoa contextual machine teaching implementation in a commutingsetting. The aim is to highlight areas in machine teaching theorythat requires more attention. The main contribution of this workis an increased focus on available features, the features space andthe potential to transfer some of the domain expert’s explanatorypowers to the machine learning system.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Machine learning, Machine Teaching, Human in the loop I
National Category
Computer Systems
Identifiers
urn:nbn:se:mau:diva-17116 (URN)10.1109/PerComWorkshops48775.2020.9156132 (DOI)000612838200047 ()2-s2.0-85091989967 (Scopus ID)978-1-7281-4716-1 (ISBN)978-1-7281-4717-8 (ISBN)
Conference
PerCom, Workshop on Context and Activity Modeling and Recognition (CoMoReA). March 23-27, 2020. Austin, Texas, USA.
Available from: 2020-04-23 Created: 2020-04-23 Last updated: 2024-02-05Bibliographically approved
2. Evaluating Interpretability in Machine Teaching
Open this publication in new window or tab >>Evaluating Interpretability in Machine Teaching
2020 (English)In: Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness: The PAAMS Collection / [ed] Springer, Springer, 2020, Vol. 1233, p. 54-65Conference paper, Published paper (Other academic)
Abstract [en]

Building interpretable machine learning agents is a challenge that needs to be addressed to make the agents trustworthy and align the usage of the technology with human values. In this work, we focus on how to evaluate interpretability in a machine teaching setting, a settingthat involves a human domain expert as a teacher in relation to a machine learning agent. By using a prototype in a study, we discuss theinterpretability denition and show how interpretability can be evaluatedon a functional-, human- and application level. We end the paperby discussing open questions and suggestions on how our results can be transferable to other domains.

Place, publisher, year, edition, pages
Springer, 2020
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1233
National Category
Human Computer Interaction
Research subject
Interaktionsdesign
Identifiers
urn:nbn:se:mau:diva-18380 (URN)10.1007/978-3-030-51999-5_5 (DOI)2-s2.0-85088540310 (Scopus ID)978-3-030-51998-8 (ISBN)978-3-030-51999-5 (ISBN)
Conference
PAAMS: International Conference on Practical Applications of Agents and Multi-Agent Systems, 7-9 October 2020, L’Aquila, Italy
Available from: 2020-09-23 Created: 2020-09-23 Last updated: 2023-07-06Bibliographically approved

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Holmberg, Lars

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