EXplainable Artificial Intelligence in Process Engineering: Promises, Facts, and Current Limitations

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Internet of things is projected to make its way into all spheres of human life in the near future. This has been compounded with the growing demand for contactless solutions in the wake of the recent pandemic. A potential solution could involve a privacy-preserving gesture-based control system that could control a wide range of appliances. Implementing such gesture-based control systems is mainly conducted using opaque box Artificial Intelligence (AI) models. Systems based on such opaque box AI models have shown high-performance metrics on in-distribution data in a lab environment. However, they are prone to failure when exposed to real-world out-of-distribution data where they cannot be tuned or calibrated due to their complexity and opaqueness. Interval Type-2 Fuzzy Logic-based explainable AI models offer an alternative to opaque box models showing comparable performance on lab in-distribution data. In contrast, in the real world, out-of-distribution data, the type-2 fuzzy models could be easily calibrated and tuned (thanks for their explainability) to provide similar performance to those achieved on the lab in-distribution data.

The rapid proliferation of artificial intelligence (AI) technologies across various industries and decision-making processes has undeniably transformed the way of approaching complex problems and tasks. AI systems have proven their prowess in areas such as healthcare, finance, and autonomous systems, revolutionizing how decisions are made. Nevertheless, this proliferation of AI has raised critical concerns regarding the transparency, accountability, and fairness of these systems, as many of the state-of-the-art AI models often resemble complex black boxes. These intricate models, particularly deep learning neural networks, harbor non-linear relationships that are difficult for human users to decipher, thereby raising concerns about bias, fairness, and overall trustworthiness in AI-driven decisions. The urgency of this issue is underscored by the realization that AI should not merely be accurate; it should also be interpretable. Explainable AI (XAI) has emerged as a vital field of research, emphasizing the development of models and techniques that render AI systems comprehensible and transparent in their decision-making processes. This paper investigates into the relevance and significance of XAI across various domains, including healthcare, finance, and autonomous systems, where the ability to understand the rationale behind AI decisions is paramount. In healthcare, where AI assists in diagnosis and treatment, the interpretability of AI models is crucial for clinicians to make informed decisions. In finance, applications like credit scoring and investment analysis demand transparent AI to ensure fairness and accountability. In the realm of autonomous systems, transparency is indispensable to guarantee safety and compliance with regulations. Moreover, government agencies in areas such as law enforcement and social services require interpretable AI to maintain ethical standards and accountability. This paper also highlights the diverse array of research efforts in the XAI domain, spanning from model-specific interpretability methods to more general approaches aimed at unveiling complex AI models. Interpretable models like decision trees and rule-based systems have gained attention for their inherent transparency, while integrating explanation layers into deep neural networks strives to balance accuracy with interpretability. The study emphasizes the significance of this burgeoning field in bridging the gap between AI's advanced capabilities and human users' need for comprehensible AI systems. It seeks to contribute to this field by exploring the design, development, and practical applications of interpretable AI models and techniques, with the ultimate goal of enhancing the trust and understanding of AI-driven decisions.

This research provides a critical analysis of how artificial intelligence (AI) influences biases in recruitment within the changing landscape of human resources and talent acquisition. It presents a comprehensive analysis and implementation of an A.I. powered resume analyzer and screening system that uses generative artificial intelligence to enhance the efficiency, fairness and scalability of the recruitment process. The core of this system is built using Google’s Gemini generative AI model, integrated with a Streamlit-based web application, designed to evaluate the quality and validity of a candidate’s resume against a given job description with precision and explainability. Instead of using traditional machine learning techniques which involve using large resume data and training and evaluating the model, this project utilizes prompt engineering and instruction-based reasoning to guide the AI model, allowing it to interpret resumes, extract relevant skills, match keywords, and generate insightful summaries without prior dataset-specific training. This technique allows for a faster and flexible development cycle. The system provides structured outputs in JSON format which includes a resume matching (JD-score) score, missing and matching keywords in the job description, candidate’s strengths and weaknesses in the resume, further resume improvement suggestions, and an XAI (Explainable AI) feature that elaborates the parameters used to calculate the score. It evaluates aspects such as the presence of key technical skills, toolsets, project relevance and domain expertise, all while reducing the human bias and human workload that often comes with manual screening. The system's XAI module further enhances the recruiter’s trust by explaining the logic behind each score, highlighting where points were awarded or deducted based on skill presence, experience, formatting, and domain relevance. While the system significantly improves speed, accuracy, and impartiality in candidate shortlisting, it relies on human intervention in assessing soft skills, cultural background, and interpersonal attributes that AI cannot effectively evaluate. Thus, this hybrid AI-human model offers an effective solution for resume analysis and recruitment process which combines AI’s speed and human intervention to evaluate soft skill, thus creating the ideal way to recruit candidates. The study concludes that by combining the analytical strength of AI with the contextual intelligence of human evaluators, organizations can create a complete hiring technology that minimizes bias, enhances decision-making, and improves transparency.

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.

In recent years, there has been a surge of interest in Artificial Intelligence (AI) systems that can provide human-centric explanations for decisions or predictions. No matter how good and efficient an AI model is, users or practitioners find it difficult to trust it if they cannot understand the AI model or its behaviours. Incorporating explainability that is human-centric in event detection systems is significant for building a decision-making process that is more trustworthy and sustainable. Human-centric and semantics-based explainable event detection will achieve trustworthiness, explainability, and reliability, which are currently lacking in AI systems. This paper provides a survey on human-centric explainable AI, explainable event detection, and semantics-based explainable event detection by answering some research questions that bother on the characteristics of human-centric explanations, the state of explainable AI, methods for human-centric explanations, the essence of human-centricity in explainable event detection, research efforts in explainable event solutions, and the benefits of integrating semantics into explainable event detection. The findings from the survey show the current state of human-centric explainability, the potential of integrating semantics into explainable AI, the open problems, and the future directions which can guide researchers in the explainable AI domain.

Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditional Battery Management Systems (BMS) that are usually on rule-based or physics-based models can hardly adapt to various chemistries, operation conditions, and fault scenarios. Approaches based on Artificial Intelligence (AI), such as machine learning (ML) and deep learning (DL), have recently been proposed to provide more promising data-driven options for fault diagnosis, state of health (SoH) estimation, and predictive optimization. This work provides a cutting-edge review of AI deployment in BESSs, which is conceptually classified into three fundamental categories: fault detection and classification, health monitoring and degradation prognosis, and intelligent control and optimization. We discuss the merits and limitations of different AI models, including supervised / unsupervised / hybrid / reinforcement learning, deployment feasibility, interpretability, and data requirements. An analysis of novel techniques, including digital twin modeling, explainable AI, and secure learning frameworks, is also included. Using comparative analysis, taxonomy visualizations, and performance summaries, this review points out the current limitations, standardization issues, and future research directions that are required for industrial-scale deployment. Through capturing the cutting-edge advancements in this field, we hope this work will serve as a guiding reference for the researchers and industry participants to design robust, scalable, and dependable AI-supported battery management systems.

Artificial Intelligence (AI) is changing the way healthcare, finance & law enforcement work by making them more efficient & creative and making it easier to make these decisions based on their information. As AI models become more complicated, their decision-making processes become less clear, which hurts trust, accountability & ethical use. Explainable AI (XAI) has come up to help with these problems by making AI systems easier to understand and more open, which helps people understand why certain actions were taken. XAI makes AI's decision-making processes easier to understand by employing these approaches including feature significance analysis, model-agnostic methodologies, interpretable models & visualization tools. These methods make sure that important tasks like medical diagnosis, approving loans & finding bias in law enforcement algorithms are all accurate, fair & easy to understand. XAI gives consumers clear, useful information that helps them make informed decisions with confidence, even if they aren't tech-savvy.  This research looks at the main ideas of XAI, including its most important methods, uses & the problems it has to solve to live up to its potential. XAI builds trust in AI systems by making AI models easier to understand. This leads to more widespread use & more responsibility in important areas. As AI becomes better, it will be important to explain things in order to deal with moral difficulties, reduce bias & make sure that people follow the rules. This will help people utilize AI technology in a more responsible & sustainable way

This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model. To develop the AI model, sentences from 900 electronic health records were labeled as positive or negative for hemorrhage and categorized into one of 12 anatomical locations. The AI model was evaluated on a test cohort consisting of 566 admissions. Using eye-tracking technology, we investigated medical doctors' reading workflow during manual chart review. Moreover, we performed a clinical use study where medical doctors read two admissions with and without AI assistance to evaluate performance when using and perception of using the AI model. The AI model had a sensitivity of 93.7% and a specificity of 98.1% on the test cohort. In the use studies, we found that medical doctors missed more than 33% of relevant sentences when doing chart review without AI assistance. Hemorrhage events described in paragraphs were more often overlooked compared with bullet-pointed hemorrhage mentions. With AI-assisted chart review, medical doctors identified 48 and 49 percentage points more hemorrhage events than without assistance in two admissions, and they were generally positive toward using the AI model as a supporting tool. Medical doctors identified more hemorrhage events with AI-assisted chart review and they were generally positive toward using the AI model.

Quantum computing (QC) is not a futuristic notion in agriculture, though its full potential has yet to be realized. QC is an emerging field at the intersection of physics and computer science that holds immense potential to revolutionize various sectors, including agriculture production and artificial intelligence (AI) modeling. While QC is still in the early stages of development and practical applications within agriculture are not yet widespread, researchers are actively exploring its potential benefits in various agricultural domains, including crop optimization, livestock breeding, and environmental monitoring. QC harnesses the principles of quantum mechanics to perform computations using quantum bits or qubits, which can exist in multiple states simultaneously. Unlike classical computers, which rely on binary bits representing 0 or 1, quantum computers exploit phenomena such as superposition and entanglement to process information in parallel, potentially offering exponential speedup for certain types of problems. In agriculture production, particularly in animal science, QC offers promising avenues for optimizing processes and enhancing productivity. Quantum algorithms can analyze vast amounts of genomic data to improve breeding programs, leading to the development of more resilient and productive livestock breeds. Furthermore, QC can facilitate precision farming techniques by modeling complex environmental factors and animal behavior to optimize feeding strategies, disease management, and overall farm management practices. Moreover, QC can significantly benefit AI modeling by accelerating computations and enabling more efficient training of AI models. Quantum algorithms can enhance the performance of AI algorithms in various tasks, including pattern recognition, natural language processing, and predictive analytics. By leveraging quantum-enhanced optimization algorithms, AI models can achieve better convergence and accuracy, leading to more effective decision-making and problem-solving capabilities. While hybrid intelligent models also represent a novel frontier in agriculture, QC has the potential to expedite the merging of mechanistic and AI modeling paradigms, facilitating a more holistic understanding of complex systems in agriculture and beyond. By integrating mechanistic models, which describe the underlying physical processes, with AI models, which learn patterns from data, quantum computing can enable comprehensive simulations and predictions of agricultural systems. This fusion of modeling paradigms can lead to more accurate and robust predictions of crop yields, livestock performance, and environmental impacts, facilitating informed decision-making for farmers and policymakers. The application of QC in agriculture, however, requires interdisciplinary collaborations between physicists, computer scientists, agronomists/animal scientists, and AI researchers. These collaborations can drive the development of quantum algorithms tailored to agricultural applications, the integration of quantum-enhanced AI techniques into existing modeling frameworks, and the deployment of QC resources in real-world agricultural systems. Ultimately, harnessing the power of QC holds the potential to revolutionize agriculture production practices, including regenerative agriculture, and advance AI modeling capabilities, paving the way for a more sustainable and efficient agricultural industry.

As artificial intelligence (AI)[1] systems become increasingly complex and pervasive, the need for transparency and interpretability has become a critical concern. Explainable AI (XAI)[2, 3] seeks to bridge the gap between opaque machine learning models and human users by providing insights into the decision-making processes of AI systems. This editorial explores the various methods employed in XAI, the challenges faced in achieving interpretability, and potential future directions for the field. The rapid adoption of AI in critical domains such as healthcare, finance, and criminal justice has raised concerns about the "black-box" nature of many AI models[4]. While these models often achieve high accuracy, their decision-making processes remain obscure, making it difficult to diagnose errors, ensure fairness, and build user trust. Explainable AI aims to address these concerns by developing techniques that offer transparency and interpretability without compromising performance.

A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model

While artificial intelligence (AI) experienced an increasing interest in industry during the past decade, the true potential and applicability of AI for automotive original equipment manufacturers (OEMs) and suppliers in real-world scenarios have not been clearly understood. Most applications of AI focus on the development of connected and autonomous cars, rather than the optimisation of automotive operations and manufacturing processes. This work, therefore, bridged this gap and shed light on the topic of AI in the context of automotive manufacturing and Industry 4.0. It aimed to promote understanding and provide up-to-date insights on specific models and methods of AI, applications that have been achieved with best practices as well as the problems that were encountered, underpinned with possible future prospects. A systematic literature review approach was adopted to ensure broad and thorough coverage of current knowledge and the identification of relevant literature on the topic. The literature search was confined to papers that were published from 2015 onwards using the databases of IEEE and ScienceDirect as primary sources, with a three-keyword search phrase to narrow down the results and increase specificity. A total of 359 papers were identified and subsequently screened for eligibility, of which 84 papers were selected for quantitative and 79 papers for qualitative analysis. The results of the quantitative analysis confirmed that the topic has markedly increased in significance, with a mere 3 papers published in 2015 and 33 papers in 2021. The majority of papers dealt with solving problems in production (39.29%), quality (35.71%) and assembly (16.67%), whereas supply chain (5.95%) and business intelligence (2.38%) were inadequately represented. The results of the qualitative analysis revealed that machine learning methods dominate current research and automotive applications, with neural networks as the most used out of more than 70 identified models. The industrial applicability was confirmed by many use cases including quality inspection, robot assembly, human–robot collaboration, material demand prediction or AI-enabled manufacturing decision making. The problems of such applications were mainly attributed to data availability and quality, model development and gaps in simulation, system integration, the complexity of automotive processes, the physical conditions of the system environment and dynamic change. For industrial applications it is thus recommended to further optimise AI methods and models, enabling a wider system integration by harvesting the potential of big data and both edge and cloud computing.KeywordsArtificial intelligenceAI models and methodsAutomotive manufacturingSystematic literature review

Unconventional reservoirs are full of uncertainty when dealing with conventional methods of modeling and analysis. The objective of this work is to use a trained artificial intelligent (AI) model to compare actual production data to AI predicted possible well production. Data-driven models based on AI are efficient tools for optimizing the production, stimulation, and completion design of wells and can be very beneficial when determining the success or failure of wells based on production. An AI model for production predictions require both native and design parameters, which include well characteristics, completion design, and stimulation design parameters.;Data from over 100 Marcellus Shale wells are used to train and test an AI model for production predictions. Feature selection algorithms are used to determine the most influential input parameters pre and post modeling for both increased model accuracy and quality assurance. Post modeling, Monte-Carlo simulation and Type Curves are used to assess the performance of each well based on the AI generated well production values. AI model generation is a very useful tool for predicting production performance of existing wells, which can be used to optimize design characteristics and reservoir production. Generating AI predictive models in fields with low amount of cases to train and test the artificial neural network require very delicate and careful considerations in order to maximize the effect and accuracy of the predictive model.;This study will be able to give an underlying method of applying these artificially intelligent solutions to a complex petroleum engineering problem. The ability to correctly apply these techniques will allow for the optimization of completion and stimulation designs within complex, unconventional reservoir.