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Publications (10 of 89) Show all publications
Riveiro, M. (2023). A design theory for uncertainty visualization?. Paper presented at Dagstuhl Seminar 22331, "Interactive Visualization for Fostering Trust in ML", August 15–19, 2022. Dagstuhl Reports, 12(8), 12-13
Open this publication in new window or tab >>A design theory for uncertainty visualization?
2023 (English)In: Dagstuhl Reports, E-ISSN 2192-5283, Vol. 12, no 8, p. 12-13Article in journal, Meeting abstract (Refereed) Published
Abstract [en]

Despite the large volume of research on uncertainty visualization, we do not fully understand the impact of uncertainty visualization on decision-making. There is evidence of both positive and negative effects of visually depicted uncertainty on decision-making.

This talk presents examples of evaluations carried out with practitioners in various application areas, including autonomous driving, air traffic risk assessment and maritime surveillance. I summarise the effects of the uncertainty visualizations provided on the users and their decision-making processes in these evaluations.

Finally, we discuss the need for a design theory/space of uncertainty visualization and elaborate on the multiple dimensions/variables that such a design space should have.

Place, publisher, year, edition, pages
Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2023
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hj:diva-60324 (URN)
Conference
Dagstuhl Seminar 22331, "Interactive Visualization for Fostering Trust in ML", August 15–19, 2022
Available from: 2023-05-08 Created: 2023-05-08 Last updated: 2023-05-09Bibliographically approved
Gleicher, M., Riveiro, M., Von Landesberger, T., Deussen, O., Chang, R. & Gillman, C. (2023). A Problem Space for Designing Visualizations. IEEE Computer Graphics and Applications, 43(4), 111-120
Open this publication in new window or tab >>A Problem Space for Designing Visualizations
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2023 (English)In: IEEE Computer Graphics and Applications, ISSN 0272-1716, E-ISSN 1558-1756, Vol. 43, no 4, p. 111-120Article in journal (Refereed) Published
Abstract [en]

Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, organize, and evaluate the things we create. The literature has many task structures (taxonomies, typologies, etc.), design spaces, and related frameworks that provide abstractions of the problems a visualization is meant to address. In this Visualization Viewpoints article, we introduce a different one, a problem space that complements existing frameworks by focusing on the needs that a visualization is meant to solve. We believe it provides a valuable conceptual tool for designing and discussing visualizations. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Abstracting, Design Analysis, Design spaces, Problem space, Specific problems, Task structure, article, taxonomy, Visualization
National Category
Computer Sciences Design
Identifiers
urn:nbn:se:hj:diva-62182 (URN)10.1109/MCG.2023.3267213 (DOI)001033529000007 ()37432777 (PubMedID)2-s2.0-85164446347 (Scopus ID);intsam;897354 (Local ID);intsam;897354 (Archive number);intsam;897354 (OAI)
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-08-18Bibliographically approved
Riveiro, M. (2023). Expectations, trust, and evaluation. Paper presented at Dagstuhl Seminar 22351 "Interactive Visualization for Fostering Trust in ML", August 28–September 2, 2022. Dagstuhl Reports, 12(8), 109-109
Open this publication in new window or tab >>Expectations, trust, and evaluation
2023 (English)In: Dagstuhl Reports, E-ISSN 2192-5283, Vol. 12, no 8, p. 109-109Article in journal, Meeting abstract (Refereed) Published
Abstract [en]

This talk focuses on the role of expectations in designing explanations from Artificial Intelligence/Machine Learning (AI/ML) -based systems. Explanations are crucial for system understanding that, in turn, are very relevant to supporting trust and trust calibration in such systems. I discuss the connections between expectations, explanations and trust in human-AI/ML system interaction.

I present two recent studies ([1, 2]) investigating if expectations modulate what people want to see and when from an AI/ML system when carrying out analytical tasks.

We found out that,

  • For matched expectations, an explanation is often not required at all, while if one is, it is of the factual type
  • For mismatched expectations, the picture is less clear, primarily because there does not seem to be a unique strategy, although mechanistic explanations are requested more often than other types

Overall, user expectations are a significant variable in determining the most suitablecontent of explanations (including whether an explanation is needed at all). More research isneeded to investigate the relationship between expectations and explanations, and how theysupport trust calibration.

Place, publisher, year, edition, pages
Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2023
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hj:diva-60323 (URN)
Conference
Dagstuhl Seminar 22351 "Interactive Visualization for Fostering Trust in ML", August 28–September 2, 2022
Available from: 2023-05-08 Created: 2023-05-08 Last updated: 2023-05-09Bibliographically approved
Pettersson, T., Riveiro, M. & Löfström, T. (2023). Explainable local and global models for fine-grained multimodal product recognition. In: : . Paper presented at Multimodal KDD 2023, International Workshop on Multimodal Learning, in conjunction with 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), August 6–10, 2023, Long Beach, CA, USA.
Open this publication in new window or tab >>Explainable local and global models for fine-grained multimodal product recognition
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Grocery product recognition techniques are emerging in the retail sector and are used to provide automatic checkout counters, reduce self-checkout fraud, and support inventory management. However, recognizing grocery products using machine learning models is challenging due to the vast number of products, their similarities, and changes in appearance. To address these challenges, more complex models are created by adding additional modalities, such as text from product packages. But these complex models pose additional challenges in terms of model interpretability. Machine learning experts and system developers need tools and techniques conveying interpretations to enable the evaluation and improvement of multimodal production recognition models.

In this work, we propose thus an approach to provide local and global explanations that allow us to assess multimodal models for product recognition. We evaluate this approach on a large fine-grained grocery product dataset captured from a real-world environment. To assess the utility of our approach, experiments are conducted for three types of multimodal models.

The results show that our approach provides fine-grained local explanations while being able to aggregate those into global explanations for each type of product. In addition, we observe a disparity between different multimodal models, in what type of features they learn and what modality each model focuses on. This provides valuable insight to further improve the accuracy and robustness of multimodal product recognition models for grocery product recognition.

Keywords
Multimodal classification, Explainable AI, Grocery product recognition, LIME, Fine-grained recognition, Optical character recognition
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hj:diva-62382 (URN)
Conference
Multimodal KDD 2023, International Workshop on Multimodal Learning, in conjunction with 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), August 6–10, 2023, Long Beach, CA, USA
Available from: 2023-09-04 Created: 2023-09-04 Last updated: 2023-09-04Bibliographically approved
Ohlander, U., Alfredson, J., Riveiro, M., Helldin, T. & Falkman, G. (2023). The Effects of Varying Degrees of Information on Teamwork a Study on Fighter Pilots. Human Factors and Ergonomics Society Annual Meeting Proceedings, 67(1), 1965-1970
Open this publication in new window or tab >>The Effects of Varying Degrees of Information on Teamwork a Study on Fighter Pilots
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2023 (English)In: Human Factors and Ergonomics Society Annual Meeting Proceedings, ISSN 1071-1813, E-ISSN 2169-5067, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, ISSN 2169-5067, Vol. 67, no 1, p. 1965-1970Article in journal (Refereed) Published
Abstract [en]

A team of fighter pilots in a distributed environment with limited access to information rely on technology to pursue teamwork. In order to design systems that support distributed teamwork, it is, therefore, necessary to understand how access to information affects the team members. Certain factors, such as mutual performance monitoring, shared mental models, adaptability, and backup behavior are considered essential for effective teamwork. We investigate these factors in this work, focusing on how visually communicated information affects fighter pilots’ perception of these factors. For that, a questionnaire including the teamwork factors in relation to certain defined scenarios that contain various levels of information was distributed to fighter pilots. We show that the studied factors are affected by the level of information available to the pilots. Especially, mutual performance monitoring increases with the degree of available information.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
teamwork, fighter pilots, information variation
National Category
Communication Studies
Identifiers
urn:nbn:se:hj:diva-62798 (URN)10.1177/21695067231192607 (DOI)
Available from: 2023-10-30 Created: 2023-10-30 Last updated: 2024-01-15Bibliographically approved
Ohlson, N.-E., Riveiro, M. & Bäckstrand, J. (2022). Identification of tasks to be supported by machine learning to reduce Sales & Operations Planning challenges in an engineer-to-order context. In: A. H. C. Ng, A. Syberfelt, D. Högberg & M. Holm (Ed.), SPS2022: Proceedings of the 10th Swedish production symposium. Paper presented at 10th Swedish Production Symposium (SPS2022), School of Engineering Science, University of Skövde, Sweden, April 26–29 2022 (pp. 39-50). Amsterdam: IOS Press
Open this publication in new window or tab >>Identification of tasks to be supported by machine learning to reduce Sales & Operations Planning challenges in an engineer-to-order context
2022 (English)In: SPS2022: Proceedings of the 10th Swedish production symposium / [ed] A. H. C. Ng, A. Syberfelt, D. Högberg & M. Holm, Amsterdam: IOS Press, 2022, p. 39-50Conference paper, Published paper (Refereed)
Abstract [en]

Sales and Operations Planning (S&OP) is a process that aims to align dimensioning efforts in a company, based on one integrated plan and with clear decision milestones. The alignment is cross-functional and connects different operations functions with each other to set an overall delivery ability. There are always challenges connecting different functions in a company which most S&OP practitioners agree with, still, that is one of the things that the S&OP-process should bridge. Digital solutions such as Enterprise Resource Planning (ERP) and other more or less sophisticated tools have contributed to an improved cross functional communication over time. S&OP in an Engineer-to-order (ETO) context, especially where engineering is a major or an equal portion as e.g., make-to-stock (MTS) and make-to-order (MTO) contexts, may experience even further challenges. Technologies within Industry 4.0 are changing the way S&OP is carried out; one of the most relevant ones is Artificial Intelligence (AI), particularly, Machine Learning (ML) that analyses data collected during these processes to find patterns and extract knowledge. The intent with this paper is to, based on S&OP-challenges, see if ML can be used to improve these challenges.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2022
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 21
Keywords
Sales & Operations Planning, Engineer to Order, Machine Learning
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:hj:diva-56329 (URN)10.3233/ATDE220124 (DOI)2-s2.0-85132814053 (Scopus ID)978-1-64368-268-6 (ISBN)978-1-64368-269-3 (ISBN)
Conference
10th Swedish Production Symposium (SPS2022), School of Engineering Science, University of Skövde, Sweden, April 26–29 2022
Available from: 2022-05-02 Created: 2022-05-02 Last updated: 2023-11-28Bibliographically approved
Riveiro, M. & Thill, S. (2022). The challenges of providing explanations of AI systems when they do not behave like users expect. In: UMAP '22: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization: . Paper presented at UMAP '22: 30th ACM Conference on User Modeling, Adaptation and Personalization, Barcelona, Spain, July 4-7, 2022 (pp. 110-120). New York: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>The challenges of providing explanations of AI systems when they do not behave like users expect
2022 (English)In: UMAP '22: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, New York: Association for Computing Machinery (ACM), 2022, p. 110-120Conference paper, Published paper (Refereed)
Abstract [en]

Explanations in artificial intelligence (AI) ensure that users of complex AI systems understand why the system behaves as it does. Expectations that users may have about the system behaviour play a role since they co-determine appropriate content of the explanations. In this paper, we investigate user-desired content of explanations when the system behaves in unexpected ways. Specifically, we presented participants with various scenarios involving an automated text classifier and then asked them to indicate their preferred explanation in each scenario. One group of participants chose the type of explanation from a multiple-choice questionnaire, the other had to answer using free text.

Participants show a pretty clear agreement regarding the preferred type of explanation when the output matches expectations: most do not require an explanation at all, while those that do would like one that explains what features of the input led to the output (a factual explanation). When the output does not match expectations, users also prefer different explanations. Interestingly, there is less of an agreement in the multiple-choice questionnaire. However, the free text responses indicate slightly favour an explanation that describes how the AI system's internal workings led to the observed output (i.e., a mechanistic explanation).

Overall, we demonstrate that user expectations are a significant variable in determining the most suitable content of explanations (including whether an explanation is needed at all). We also find different results, especially when the output does not match expectations, depending on whether participants answered via multiple-choice or free text. This shows a sensitivity to precise experimental setups that may explain some of the variety in the literature.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2022
Keywords
factual, explainable AI, counterfactual, mechanistic, explanations, expectations
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-57996 (URN)10.1145/3503252.3531306 (DOI)2-s2.0-85135173255 (Scopus ID)978-1-4503-9207-5 (ISBN)
Conference
UMAP '22: 30th ACM Conference on User Modeling, Adaptation and Personalization, Barcelona, Spain, July 4-7, 2022
Available from: 2022-07-20 Created: 2022-07-20 Last updated: 2022-08-10Bibliographically approved
Ohlson, N.-E., Bäckstrand, J. & Riveiro, M. (2021). Artificial Intelligence-enhanced Sales & Operations Planning in an Engineer-to-order context. In: : . Paper presented at PLANs forsknings- och tillämpningskonferens 2021, Högskolan i Borås, 20-21 oktober 2021.
Open this publication in new window or tab >>Artificial Intelligence-enhanced Sales & Operations Planning in an Engineer-to-order context
2021 (English)Conference paper, Oral presentation with published abstract (Refereed)
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:hj:diva-55609 (URN)
Conference
PLANs forsknings- och tillämpningskonferens 2021, Högskolan i Borås, 20-21 oktober 2021
Available from: 2022-01-18 Created: 2022-01-18 Last updated: 2023-11-28Bibliographically approved
Ulfenborg, B., Karlsson, A., Riveiro, M., Andersson, C. X., Sartipy, P. & Synnergren, J. (2021). Multi-assignment clustering: Machine learning from a biological perspective. Journal of Biotechnology, 326, 1-10
Open this publication in new window or tab >>Multi-assignment clustering: Machine learning from a biological perspective
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2021 (English)In: Journal of Biotechnology, ISSN 0168-1656, E-ISSN 1873-4863, Vol. 326, p. 1-10Article in journal (Refereed) Published
Abstract [en]

A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Clustering, K-means, annotation enrichment, multiple cluster assignment, pathways, transcriptomics
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-51257 (URN)10.1016/j.jbiotec.2020.12.002 (DOI)000616124700001 ()33285150 (PubMedID)2-s2.0-85097644109 (Scopus ID)HOA (Local ID)HOA (Archive number)HOA (OAI)
Funder
Knowledge Foundation, 2014/0301, 2017/0302
Available from: 2020-12-17 Created: 2020-12-17 Last updated: 2021-03-15Bibliographically approved
Ventocilla, E., Martins, R. M., Paulovich, F. & Riveiro, M. (2021). Scaling the Growing Neural Gas for Visual Cluster Analysis. Big Data Research, Article ID 100254.
Open this publication in new window or tab >>Scaling the Growing Neural Gas for Visual Cluster Analysis
2021 (English)In: Big Data Research, ISSN 2214-5796, E-ISSN 2214-580X, article id 100254Article in journal (Refereed) Published
Abstract [en]

The growing neural gas (GNG) is an unsupervised topology learning algorithm that models a data space through interconnected units that stand on the most populated areas of that space. Its output is a graph that can be visually represented on a two-dimensional plane, disclosing cluster patterns in datasets. It is common, however, for GNG to result in highly connected graphs when trained on high-dimensional data, which in turn leads to highly cluttered 2D representations that may fail to disclose meaningful patterns. Moreover, its sequential learning limits its potential for faster executions on local datasets, and, more importantly, its potential for training on distributed datasets while leveraging from the computational resources of the infrastructures in which they reside.

This paper presents two methods that improve GNG for the visualization of cluster patterns in large-scale and high-dimensional datasets. The first one focuses on providing more accurate and meaningful 2D visual representations for cluster patterns of high-dimensional datasets, by avoiding connections that lead to high-dimensional graphs in the modeled topology which may, in turn, result in overplotting and clutter. The second method presented in this paper enables the use of GNG on big and distributed datasets with faster execution times, by modeling and merging separate parts of a dataset using the MapReduce model.

Quantitative and qualitative evaluations show that the first method leads to the creation of lower-dimensional graph structures that provide more meaningful (and sometimes more accurate) cluster representations with less overplotting and clutter; and that the second method preserves the accuracy and meaning of the cluster representations while enabling its execution in large-scale and distributed settings.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Growing neural gas, Big data, Visual analytics, Unsupervised learning, Exploratory data analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-54283 (URN)10.1016/j.bdr.2021.100254 (DOI)000710458600012 ()2-s2.0-85113545584 (Scopus ID)HOA;intsam;758509 (Local ID)HOA;intsam;758509 (Archive number)HOA;intsam;758509 (OAI)
Available from: 2021-08-19 Created: 2021-08-19 Last updated: 2021-11-15Bibliographically approved
Projects
Virtual factories with knowledge-driven optimization (VF-KDO); University of Skövde
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2900-9335

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