Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research

The final search query for the Systematic Literature Review (SLR) was conducted on 15th July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of Science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explanation, which are format (explanation representation format), completeness (explanation should contain all required information, including the supplementary information), accuracy (information regarding the accuracy of the explanation), and currency (explanation should contain recent information). Moreover, along with the automatic representation of the explanation, the users can request additional information if needed. We have also found five dimensions of XAI effects: trust, transparency, understandability, usability, and fairness. In addition, we investigated current knowledge from selected articles to problematize future research agendas as research questions along with possible research paths. Consequently, a comprehensive framework of XAI and its possible effects on user behavior has been developed

Translate Data Into Meaning: integration of meteorology and geomatics to generate meaningful information for decision makers

A variety of actors at all scales and acting in different domains such as emergency management, agriculture, sports and leisure and commercial activities, are becoming more aware of the challenges and opportunities that meteorological data analysis poses for their operational goals. The increasing availability of meteorological data coupled with a rapid improvement in technology led to the widespread dissemination of the weather information to a variety of users on a regular basis. Particularly through the internet and mobile application all users, despite their varied background, can access to big amount of data with a high potential to gather essential input that can significantly help their decisions. At the same time, simply creating and disseminating information without context does not necessarily offer an added value to sèecific users. One of the main issues is related to the scientific approach of weather analysis and to the representation of results, which are hardly understandable for non-technical users and therefore not easily usable to make decisions. As a result, there are several researches aiming at finding new ways of supporting decision making by supplying easy to use information. The main objective of this thesis is therefore to provide guidance on how to identify and characterize the needs for meaningful and usable information among various users of meteorology, including members of the public, emergency managers, other government decision makers, and private-sector entities, both direct users and intermediaries. In particular a methodology for the integration of meteorological data and GIS capabilities is investigated and applied to three different end users having similarities and differences. Scientific analysis, results and cartographic products are adapted to specific requirements, experience and perceptions of the three different users

Explainable Artificial Intelligence in Data Science: From Foundational Issues Towards Socio-technical Considerations

A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already afects many sensitive realms (as in Politics, Social Sciences, or Law). The decision making power handover to opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solu tions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientifc and humanistic areas. The frst aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is benefciated from part of the scientifc and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the feld of Data Science. The second objective is to develop some arguments justifying the authors’ bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justifcation and the explainer-explainee agreement.Agencia Estatal de Investigación PID2019-109152GB-I00/AEI/10.13039/50110001103

Five feet high and rising : cities and flooding in the 21st century

Urban flooding is an increasingly important issue. Disaster statistics appear to show flood events are becoming more frequent, with medium-scale events increasing fastest. The impact of flooding is driven by a combination of natural and human-induced factors. As recent flood events in Pakistan, Brazil, Sri Lanka and Australia show, floods can occur in widespread locations and can sometimes overwhelm even the best prepared countries and cities. There are known and tested measures for urban flood risk management, typically classified as structural or engineered measures, and non-structural, management techniques. A combination of measures to form an integrated management approach is most likely to be successful in reducing flood risk. In the short term and for developing countries in particular, the factors affecting exposure and vulnerability are increasing at the fastest rate as urbanization puts more people and more assets at risk. In the longer term, however, climate scenarios are likely to be one of the most important drivers of future changes in flood risk. Due to the large uncertainties in projections of climate change, adaptation to the changing risk needs to be flexible to a wide range of future scenarios and to be able to cope with potentially large changes in sea level, rainfall intensity and snowmelt. Climate uncertainty and budgetary, institutional and practical constraints are likely to lead to a combining of structural and non-structural measures for urban flood risk management, and arguably, to a move away from what is sometimes an over-reliance on hard-engineered defenses and toward more adaptable and incremental non-structural solutions.Hazard Risk Management,Wetlands,Natural Disasters,Adaptation to Climate Change,Climate Change Impacts

Model Blindness: Investigating a model-based route-recommender system’s impact on decision making

Model-Based Decision Support Systems (MDSS) are prominent in many professional domains of high consequence, such as aeronautics, emergency management, military command and control, healthcare, nuclear operations, intelligence analysis, and maritime operations. An MDSS generally uses a simplified model of the task and the operator to impose structure to the decision-making situation and provide information cues to the operator that is useful for the decision-making task. Models are simplifications, can be misspecified, and have errors. Adoption and use of these errorful models can lead to the impoverished decision-making of users. I term this impoverished state of the decision-maker model blindness. A series of two experiments were conducted to investigate the consequences of model blindness on human decision-making and performance and how those consequences can be mitigated via an explainable AI (XAI) intervention. The experiments implemented a simulated route recommender system as an MDSS with a true data-generating model (unobservable world model). In Experiment 1, the true model generating the recommended routes was misspecified to different levels to impose model blindness on users. In Experiment 2, the same route-recommender system was employed with a mitigation technique to overcome the impact of model-misspecifications on decision-making. Overall, the results of both experiments provide little support for performance degradation due to model blindness imposed by misspecified systems. The XAI intervention provided valuable insights into how participants adjusted their decision-making to account for bias in the system and deviated from choosing the model-recommended alternatives. The participants' decision strategies revealed that they could understand model limitations from feedback and explanations and could adapt their strategy to account for those misspecifications. The results provide strong support for evaluating the role of decision strategies in the model blindness confluence model. These results help establish a need for carefully evaluating model blindness during the development, implementation, and usage stages of MDSS.Ph.D

Artificial intelligence in supply chain decision-making: An environmental, social, and governance triggering and technological inhibiting protocol

Purpose Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors. Design/methodology/approach Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain. Findings In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers. Research limitations/implications Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias. Originality/value The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area

Five feet high and rising: Cities and flooding in the 21st Century

Urban flooding is an increasingly important issue.Disaster statistics appear to show flood events arebecoming more frequent, with medium-scale eventsincreasing fastest. The impact of flooding is driven bya combination of natural and human-induced factors.As recent flood events in Pakistan, Brazil, Sri Lanka andAustralia show, floods can occur in widespread locationsand can sometimes overwhelm even the best preparedcountries and cities. There are known and tested measuresfor urban flood risk management, typically classified asstructural or engineered measures, and non-structural,management techniques. A combination of measures toform an integrated management approach is most likelyto be successful in reducing flood risk. In the short termand for developing countries in particular, the factorsaffecting exposure and vulnerability are increasing atthe fastest rate as urbanization puts more people andmore assets at risk. In the longer term, however, climatescenarios are likely to be one of the most importantdrivers of future changes in flood risk. Due to the largeuncertainties in projections of climate change, adaptationto the changing risk needs to be flexible to a wide rangeof future scenarios and to be able to cope with potentiallylarge changes in sea level, rainfall intensity and snowmelt.Climate uncertainty and budgetary, institutional andpractical constraints are likely to lead to a combining ofstructural and non-structural measures for urban floodrisk management, and arguably, to a move away fromwhat is sometimes an over-reliance on hard-engineereddefenses and toward more adaptable and incrementalnon-structural solutions

Machine Learning-powered Artificial Intelligence in Arms Control

Artificial intelligence (AI), especially AI driven by machine learning, is on everyone’s lips. Even in armaments such systems are playing an increasingly important role: Some weapons systems are already able to identify targets independently and engage in combat with them. This poses problems for traditional forms of arms control originally designed to monitor physical objects such as mines and small arms and their internal function. In addition, important additional effects of reliable control such as confidence- building and stabilization of diplomatic relations are not addressed. It is important for arms control to address such risks as well. At the same time, the deployment of Machine Learning-powered Artificial Intelligence (MLpAI) as a tool offers tremendous potential for improving arms control processes. Here, more precise and comprehensive data processing can engender more trust between states in particular. This tension between the risks and the opportunities connected with the use of MLpAI in arms control is highlighted in this report

Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc

Game Theory and Prescriptive Analytics for Naval Wargaming Battle Management Aids

NPS NRP Technical ReportThe Navy is taking advantage of advances in computational technologies and data analytic methods to automate and enhance tactical decisions and support warfighters in highly complex combat environments. Novel automated techniques offer opportunities to support the tactical warfighter through enhanced situational awareness, automated reasoning and problem-solving, and faster decision timelines. This study will investigate how game theory and prescriptive analytics methods can be used to develop real-time wargaming capabilities to support warfighters in their ability to explore and evaluate the possible consequences of different tactical COAs to improve tactical missions. This study will develop a conceptual design of a real-time tactical wargaming capability. This study will explore data analytic methods including game theory, prescriptive analytics, and artificial intelligence (AI) to evaluate their potential to support real-time wargaming.N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.