On the Multiple Roles of Ontologies in Explainable AI

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness

On the Multiple Roles of Ontologies in Explainable AI

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness

Supporting the Billing Process in Outpatient Medical Care: Automated Medical Coding Through Machine Learning

Reimbursement in medical care implies significant administrative effort for medical staff. To bill the treatments or services provided, diagnosis and treatment codes must be assigned to patient records using standardized healthcare classification systems, which is a time-consuming and error-prone task. In contrast to ICD diagnosis codes used in most countries for inpatient care reimbursement, outpatient medical care often involves different reimbursement schemes. Following the Action Design Research methodology, we developed an NLP-based machine learning artifact in close collaboration with a general practitioner’s office in Germany, leveraging a dataset of over 5,600 patients with more than 63,000 billing codes. For the code prediction of most problematic treatments as well as a complete code prediction task, we achieved F1-scores of 93.60 % and 78.22 %, respectively. Throughout three iterations, we derived five meta requirements leading to three design principles for an automated coding system to support the reimbursement of outpatient medical care

In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models

Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency Escherichia coli (E. coli) monitoring can help prevent diseases from fecal pathogenic microorganisms, such monitoring is time-consuming and expensive. Process-driven models are an alternative means for estimating concentrations of fecal pathogens. However, process-based modeling still has limitations in improving the model accuracy because of the complexity of relationships among hydrological and environmental variables. With the rise of data availability and computation power, the use of data-driven models is increasing. In this study, we simulated fate and transport of E. coli in a 0.6 km(2) tropical headwater catchment located in the Lao People's Democratic Republic (Lao PDR) using a deep-learning model and a process-based model. The deep learning model was built using the long short-term memory (LSTM) methodology, whereas the process-based model was constructed using the Hydrological Simulation Program-FORTRAN (HSPF). First, we calibrated both models for surface as well as for subsurface flow. Then, we simulated the E. coli transport with 6 min time steps with both the HSPF and LSTM models. The LSTM provided accurate results for surface and subsurface flow with 0.51 and 0.64 of the Nash-Sutcliffe efficiency (NSE) values, respectively. In contrast, the NSE values yielded by the HSPF were -0.7 and 0.59 for surface and subsurface flow. The simulated E. coli concentrations from LSTM provided the NSE of 0.35, whereas the HSPF gave an unacceptable performance with an NSE value of -3.01 due to the limitations of HSPF in capturing the dynamics of E. coli with land-use change. The simulated E. coli concentration showed the rise and drop patterns corresponding to annual changes in land use. This study showcases the application of deep-learning-based models as an efficient alternative to process-based models for E. coli fate and transport simulation at the catchment scale

A Cognitive Work Analysis Approach to Explainable Artificial Intelligence in Non-Expert Financial Decision-Making

Artificial Intelligence (AI) is being increasingly used to assist complex decision-making such as financial investing. As most AI systems rely on black-box machine learning models, understanding how to support human decision-makers and gaining users' trust becomes important. Explainable Artificial Intelligence (XAI) has been proposed to address these issues by making the decision-making process of AI systems understandable to users. However, existing XAI approaches fail to take into account users' domain experience, and fail to support users with limited domain expertise. This work aims to fill this gap. We presented an approach to integrate domain expertise into XAI, and showed that this approach can have a number of benefits to users of XAI systems such as improved task performance and better assessment of XAI. The main contributions of this work include identifying the benefits of adding domain knowledge to XAI, demonstrating the usefulness of Cognitive Work Analysis (CWA) in XAI, and developing recommendations for future design of AI systems. First, through a Work Domain Analysis (WDA) approach, we identified opportunities to improve the existing XAI approaches by augmenting the explanations with domain knowledge and conducted an online study with 100 participants on users' perceptions of AI in a credit approval context. Results showed some benefits in improving user perceptions and highlighted the importance of contextual factors. Next, we introduced a testbed for exploring user behavior and task performance in a financial decision-making task. We designed decision-support aids based on domain knowledge and explored their effectiveness in an experimental study with 60 participants. In the study, participants engaged with an AI assistant and made investing decisions. Depending on the condition, participants had access to domain knowledge presented on a separate display, domain knowledge embedded in the AI assistant, or no access to domain knowledge. The results showed that participants who had access to domain knowledge relied less on AI when it was incorrect, and obtained better task performance. The effect of domain knowledge on perceptions of AI was limited. Next, we analyzed the user interviews that were part of the previous study. We identified users' mental models of AI and multiple ways they integrated the AI into their decision-making process. The analysis also revealed the complexity of designing for non-expert users, and we developed recommendations for future research and design. Finally, we conducted a Control Task Analysis and Strategies Analysis to synthesize the qualitative and quantitative findings and developed decision ladders and information flow maps. The analyses provided insights into the influence of AI on the decision-making process, challenges associated with non-expert users, and opportunities to improve AI user interface design