Högskolan i Skövde

AI tools for supporting, or even fully automating, human decision-making have been proposed in a variety of domains, promising faster and better quality of decisions. However, for high-stakes and critical decisions, humans are still required in the decision-making process. Despite the need for human involvement, the research core centers mainly around the technical issues of AI, i.e. how to develop better performing machine learning (ML) models, setting aside the issue of designing, developing, and evaluating AI tools that are to be used in a human-AI context. This focus has led to a lack of experience and guidance of designing and developing AI tools that support their users in a decision-making context, keeping the human in the loop. In this paper, we outline our work on designing, developing, and evaluating a transparent AI-based tool to be used by non-AI experts, namely healthcare professionals. The work carried out had two parallel tracks. One focused on testing and implementing a suitable ML technique for sepsis diagnostics based on real patient data and applying explainable AI (XAI) techniques on the results to better enable healthcare professionals to understand and trust the analysis results. The other track included an iterative design process for developing a user-centered, transparent, and trustworthy sepsis diagnostic tool, evaluating whether the generated XAI explanations were fit for purpose. We present the process applied for intertwining these tracks during a common multidisciplinary development process, providing guidance how to conduct a human-centered AI (HCAI) project. We discuss lessons learned, and outline future work for the development of HCAI tools to be used by non-AI experts.

Climate change has impacted global temperatures, triggering extreme weather and adverse environmental effects. In Sweden, these changes have caused shifts in weather patterns, leading to disruptions in infrastructure. This, in turn, has influenced water turbidity levels, negatively impacting water quality. To tackle these issues, a study was conducted using machine learning to predict turbidity with six meteorological variables collected for two years. Our preliminary research showed a substantial influence of seasonal changes on water turbidity, especially air temperature. Identifying supporting indicators such as lagged features is crucial and considerably improved the turbidity prediction performance for two of the machine learning models used. However, the study also identified challenges like data collection and uncertainty issues. We recommend improving data collection quality with higher frequency, minimizing geographical gaps between data collection points, sharing calibration assumptions, checking the sensors regularly, and accounting for data anomalies. Understanding these challenges and their potential implications could lead to more methodological enhancements.

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. © 2023 Human Factors and Ergonomics Society.

To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.

Aggregation functions are extensively used in decision making processes to combine available information. Arithmetic mean and weighted mean are some of the most used ones. In order to use a weighted mean, we need to define its weights. The Analytical Hierarchy Process (AHP) is a well known technique used to obtain weights based on interviews with experts. From the interviews we define a matrix of pairwise comparisons of the importance of the weights. We call these AHP-like matrices absolute preferences of weights. We propose another type of matrix that we call a relative preference matrix. We define this matrix with the same goal—to find the weights for weighted aggregators. We discuss how it can be used for eliciting the weights for the weighted mean and define a similar approach for the Choquet integral.

To ensure that a wireless telecommunication system is reliably functioning at all times, root-causes of potential network failures need to be identified and remedied, ideally before a noticeable network performance degradation occurs. Network operators are today observing a multitude of key performance indicators (KPIs) and are notified of possible network problems through alarms issued by different parts of the network. However, the number of cascading alarms together with the number of observable KPIs are easily overwhelming the operator’s cognitive capacity. In this paper we show how exploratory data analysis and machine learning, in particular topic modelling, can assist the operator when monitoring network performance and identifying anomalous network behaviour as well as supporting the operator’s analysis of the anomaly and identification of its root-cause. 

In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

This report outlines trends and application areas within the research field of intelligent user interfaces(IUIs) from 2010-2018. The purpose of the report is to give an overview of the IUI research area andpoint out particular subfields that have been given attention in the recent years, indicating possible trendsfor future research. Our report indicates that the field of IUIs is very broad, resulting in rather diverseresearch trends within the area. However, general trends could be identified, such as an increasing interest inbetter human-machine decision-making, where strategies for explaining the automatic reasoning are beinginvestigated together with ways of improving the trustworthiness of the systems and their possible adaptationsto individuals’ needs. The report also outlines research on multimodal interactions, adaptivity and humanrobotcollaboration, addressing challenges such as increased human workload, unobtrusiveness, privacy andmultiparty communication.

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

The Analytical Hierarchy Process (AHP) has been extensively used to interview experts in order to find the weights of the criteria. We call AHP-like matrices relative preferences of weights. In this paper we propose another type of matrix that we call a absolute preference matrix. They are also used to find weights, and we propose that they can be applied to find the weights of weighted means and also of the Choquet integral.