Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons

[EN] Internet of Things (IoT) widely use analysis of data with artificial intelligence (AI) techniques in order to learn from user actions, support decisions, track relevant aspects of the user, and notify certain events when appropriate. However, most AI techniques are based on mathematical models that are difficult to understand by the general public, so most people use AI-based technology as a black box that they eventually start to trust based on their personal experience. This article proposes to go a step forward in the use of AI in IoT, and proposes a novel approach within the Human-centric AI field for generating explanations about the knowledge learned by a neural network (in particular a multilayer perceptron) from IoT environments. More concretely, this work proposes two techniques based on the analysis of artificial neuron weights, and another technique aimed at explaining each estimation based on the analysis of training cases. This approach has been illustrated in the context of a smart IoT kitchen that detects the user depression based on the food used for each meal, using a simulator for this purpose. The results revealed that most auto-generated explanations made sense in this context (i.e. 97.0%), and the execution times were low (i.e. 1.5 ms or lower) even considering the common configurations varying independently the number of neurons per hidden layer (up to 20), the number of hidden layers (up to 20) and the number of training cases (up to 4,000).This work was supported in part by the U.K. Engineering and Physical Sciences Research under Grant EP/N028155/1, in part by the Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo (CYTED) through the CITIES: Ciudades inteligentes totalmente integrales, eficientes y sotenibles under Grant 518RT0558, and in part by the Spanish council of Science, Innovation and Universities from the Spanish Government through the Diseno colaborativo para la promocion del bienestar en ciudades inteligentes inclusivas under Grant TIN2017-88327-R.García-Magariño, I.; Muttukrishnan, R.; Lloret, J. (2019). Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons. IEEE Access. 7:125562-125574. https://doi.org/10.1109/ACCESS.2019.2937521S125562125574

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.Comment: 29 pages, 7 figures, 2 tables. IEEE Open Journal of the Communications Society (2022

XAI Sustainable Human in the Loop Maintenance

This is the final version. Available on open access from Elsevier via the DOI in this recordThe field of Explainable Artificial Intelligence (XAI) is a relatively new approach to AI, with the aim to provide black box algorithms with human intelligible narrative functionality. It is most often in end-of-life considerations of the asset lifecycle that sustainability issues are encountered. Modern maintenance practice requires a holistic understanding of lifecycle and options for sustainable asset treatments. human in the loop solutions offer a way to leverage both machine and human skill sets to provide the next level of automaton solutions for industrial maintenance activities. This paper presents a framework for human in the loop Intelligent and Sustainable Maintenance. In bridging the gap between machines and humans XAI leverages the best of both worlds to provide a new level of agility to cyber assisted maintenance activities and full lifecycle consideration of assets; a notion that is necessary throughout the organization in the achievement of sustainability goals set by governments around the world in the achievement of a net zero carbon emission economy

Setting the relationship between human-centered approaches and users? Digital well-being: A review

With the advancement of technology and advent of the new digital era, the society is getting increasingly exposed to novel technologies, digital platforms, or smart devices. This reality opens a wide range of questions about the benefits and challenges of technology and its impact on humans. In this context, the present study investigates the relationship between human-centered approaches and their application to achieve users' digital well-being, as well as explores whether marketing and business industry are sufficiently considering human-centered approaches in their implementation of practices that care for users' digital wellbeing. To this end, we conduct a systematic literature review. The exploratory results confirm that the implementation of human-centered approaches makes it possible to achieve a greater user well-being in the marketing and management sector. Additionally, we also identify and dis-cuss seven more relevant areas. Our review concludes with a discussion of the theoretical and practical implications of our findings for further research on the use of human-centric and digital well-being concepts.info:eu-repo/semantics/publishedVersio

Who needs XAI in the Energy Sector? A Framework to Upgrade Black Box Explainability

Artificial Intelligence (AI)-based methods in the energy sector challenge companies, organizations, and societies. Organizational issues include traceability, certifiability, explainability, responsibility, and efficiency. Societal challenges include ethical norms, bias, discrimination, privacy, and information security. Explainable Artificial Intelligence (XAI) can address these issues in various application areas of the energy sector, e.g., power generation forecasting, load management, and network security operations. We derive Key Topics (KTs) and Design Requirements (DRs) and develop Design Principles (DPs) for efficient XAI applications through Design Science Research (DSR). We analyze 179 scientific articles to identify our 8 KTs for XAI implementation through text mining and topic modeling. Based on the KTs, we derive 15 DRs and develop 18 DPs. After that, we discuss and evaluate our results and findings through expert surveys. We develop a Three-Forces Model as a framework for implementing efficient XAI solutions. We provide recommendations and a further research agenda

An artificial intelligence-based collaboration approach in industrial IoT manufacturing : key concepts, architectural extensions and potential applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented

Deep and transfer learning for building occupancy detection: A review and comparative analysis

The building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT is essential for data analysis and intelligent decision-making. Thus, data-driven approaches to infer occupancy patterns usage are gaining growing interest in BIoT applications. Typically, analyzing big occupancy data gathered by BIoT networks helps significantly identify the causes of wasted energy and recommend corrective actions. Within this context, building occupancy data aids in the improvement of the efficacy of energy management systems, allowing the reduction of energy consumption while maintaining occupant comfort. Occupancy data might be collected using a variety of devices. Among those devices are optical/thermal cameras, smart meters, environmental sensors such as carbon dioxide (CO2), and passive infrared (PIR). Even though the latter methods are less precise, they have generated considerable attention owing to their inexpensive cost and low invasive nature. This article provides an in-depth survey of the strategies used to analyze sensor data and determine occupancy. The article's primary emphasis is on reviewing deep learning (DL), and transfer learning (TL) approaches for occupancy detection. This work investigates occupancy detection methods to develop an efficient system for processing sensor data while providing accurate occupancy information. Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. The main concerns affecting the current occupancy detection system in terms of privacy and precision were thoroughly discussed. For occupancy detection, several directions were provided to avoid or reduce privacy problems by employing forthcoming technologies such as edge devices, Federated learning, and Blockchain-based IoT. 2022 The AuthorsThis paper was made possible by the Graduate Assistant-ship (GA) program provided from Qatar University (QU). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.Scopu

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Artificial intelligence (AI) has recently been used frequently, especially concerning the Internet of Things (IoT). However, IoT devices cannot work alone, assisted by Low Power Wide Area Network (LPWAN) for long-distance communication and Short-Range Network for a short distance. However, few reviews about AI can help LPWAN and Short-Range Network. Therefore, the author took the opportunity to do this review. This study aims to review LPWAN and Short-Range Networks AI papers in systematically enhancing IoT performance. Reviews are also used to systematically maximize LPWAN systems and Short-Range networks to enhance IoT quality and discuss results that can be applied to a specific scope. The author utilizes selected reporting items for systematic review and meta-analysis (PRISMA). The authors conducted a systematic review of all study results in support of the authors' objectives. Also, the authors identify development and related study opportunities. The author found 79 suitable papers in this systematic review, so a discussion of the presented papers was carried out. Several technologies are widely used, such as LPWAN in general, with several papers originating from China. Many reports from conferences last year and papers related to this matter were from 2020-2021. The study is expected to inspire experimental studies in finding relevant scientific papers and become another review

Human-centric artificial intelligence for supporting finance decisions

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2019/2020Este Trabajo de Fin de Grado se ha centrado en el desarrollo de una inteligencia artificial centrada en los humanos para la toma de decisiones financieras, concretamente, la concesión de una hipoteca. Para el desarrollo de este proyecto, nos hemos adentrado en el ámbito de la Inteligencia Artificial centrada en los Humanos para acompañar la decisión tomada por los algoritmos de Inteligencia Artificial empleados para llevar a cabo dicha decisión financiera con una explicación autogenerada acerca del conocimiento aprendido por los mismos y que justifique dicha decisión financiera, proporcionando a los posibles usuarios de la inteligencia artificial una explicación fácil de entender acerca de las decisiones tomadas. En esta memoria, se describe con detalle el desarrollo del proyecto.This Final Degree Project has focused on the development of a Human-centric Artificial Intelligence approach for financial decision-making, specifically, the granting of a mortgage. For the development of this project, we have entered the field of Human-centric Artificial Intelligence to accompany the decision made by the Artificial Intelligence algorithms used to carry out said financial decision with a self-generated explanation about the knowledge learned by themselves and that justifies such financial decision, providing potential users of this Artificial Intelligence approach with an easy-to-understand explanation of the decisions made. In this report, the development of the project is described in detail.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu