Search results for: artificial intelligence ethics and bias

Authors: Belkacem Laimouche

Abstract:

With the field of Artificial Intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: Artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, inter-laboratory comparison, data analysis, data reliability, bias impact assessment, bias measurement.

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Authors: A. Elagez, R. Monib

Abstract:

This empirical study investigates the observed risks associated with adopting Artificial Intelligence (AI) technologies in the construction industry and proposes potential mitigation strategies. While AI has transformed several industries, the construction industry is slowly adopting advanced technologies like AI, introducing new risks that lack critical analysis in the current literature. A comprehensive literature review identified a research gap, highlighting the lack of critical analysis of risks and the need for a framework to measure and mitigate the risks of AI implementation in the construction industry. Consequently, with two distinct surveys (n = 15 and n = 45), and semi-structured interviews with four industry professionals, this research reveals significant disparities in AI adoption readiness between research and industry sectors. Survey results showed 87% familiarity with AI among research professionals compared to only 18% among industry practitioners, with corresponding adoption rates of 67% and 11% respectively. The study identified key barriers to AI adoption, with lack of awareness, insufficient knowledge and skills, and high implementation costs ranking as the most significant challenges. The most critical risks associated with AI use were identified as data security/privacy, lack of human control, algorithmic bias, and accountability. Additionally, the research revealed that 67% of research-oriented respondents believed Trustworthy AI could help manage risks, while 75% supported Explainable AI as a risk mitigation tool. These findings emphasize the necessity for tailored risk assessment frameworks, guidelines, and governance principles to address the identified risks and promote the responsible adoption of AI technologies in the construction sector.

Keywords: Applications of artificial intelligence in construction, artificial intelligence ethics and bias, artificial intelligence risks in construction, risk management.

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Authors: Lipeng Zhang, Limei Li, Yanming Pearl Zhang

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This paper takes a philosophical view as axiom, and reveals the relationship between information theory and Natural Intelligence and Artificial Intelligence under real world conditions. This paper also derives the relationship between natural intelligence and nature. According to communication principle of information theory, Natural Intelligence can be divided into real part and virtual part. Based on information theory principle that Information does not increase, the restriction mechanism of Natural Intelligence creativity is conducted. The restriction mechanism of creativity reveals the limit of natural intelligence and artificial intelligence. The paper provides a new angle to observe natural intelligence and artificial intelligence.

Keywords: Natural intelligence, artificial intelligence, creativity, information theory.

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Authors: Mitchell J. McHugh, John J. Case

Abstract:

Systems engineering is a holistic discipline that seeks to organize and optimize complex, interdisciplinary systems. With the growth of artificial intelligence, systems engineers must face the challenge of leveraging artificial intelligence systems to solve complex problems. This paper analyzes the integration of systems engineering and artificial intelligence and discusses how artificial intelligence systems embody the systems decision process (SDP). The SDP is a four-stage problem-solving framework that outlines how systems engineers can design and implement solutions using value-focused thinking. This paper argues that artificial intelligence models can replicate the SDP, thus validating its flexible, value-focused foundation. The authors demonstrate this by developing a machine vision mobile application that can classify weapons to augment the decision-making role of an Army subject matter expert. This practical application was an end-to-end design challenge that highlights how artificial intelligence systems embody systems engineering principles. The impact of this research demonstrates that the SDP is a dynamic tool that systems engineers should leverage when incorporating artificial intelligence within the systems that they develop.

Keywords: Computer vision, machine learning, mobile application, systems engineering, systems decision process.

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Authors: Khaled M. Alhawiti

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This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: Speech recognition, acoustic phonetic, artificial intelligence, Hidden Markov Models (HMM), statistical models of speech recognition, human machine performance.

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Authors: Pejman Hosseinioun, Rose Shayeghi, Ghasem Ghorbani Rostam

Abstract:

Nowadays, obtaining traditional statistics and reports is not adequate for the needs of organizational managers. The managers need to analyze and to transform the raw data into knowledge in the world filled with information. Therefore in this regard various processes have been developed. In the meantime the artificial intelligence-based processes are used and the new topics such as business intelligence and knowledge discovery have emerged. In the current paper it is sought to study the business intelligence and its applications in the organizations.

Keywords: Business intelligence, business intelligence infrastructures, business processes.

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Authors: Shankar Kambhampaty, Harish Rohan Kambhampaty

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Data in IT systems in enterprises have been growing at phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several Artificial Intelligence/Machine Learning (AI/ML) and Business Intelligence (BI) tools and technologies available in marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information and intelligence components and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations.

Keywords: Architecture, model, intelligence, artificial intelligence, business intelligence, AI, BI, ML, analytics, enterprise.

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Authors: Mokrane Selma

Abstract:

The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: Bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network.

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Authors: R. Merlo, M. González, Z. Rivero, L. González

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This exploratory work aimed to understand university students’ perceptions of the use of artificial intelligence (AI) during their time in the classroom. The significance of using AI in education, the degree of interest, knowledge acquisition, and how it would influence an interactive resource for acquiring skills were explored. Within this framework, a test with 30 items was designed and administered to 800 volunteer first-year university students of natural and exact sciences. Based on a randomized pilot test, it was validated with Cronbach's alpha coefficient. Descriptive statistics of the sample used allowed us to observe the preponderance of the dimensions that constitute the attitude construct. Subsequently, factor analysis by dimensions provided insights into the students' habits, according to the knowledge acquired and the emotions engaged during the topics developed in the classroom.

Keywords: Attitude, artificial intelligence, didactics, teaching.

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Authors: N.R.N. Idris

Abstract:

This study uses simulated meta-analysis to assess the effects of publication bias on meta-analysis estimates and to evaluate the efficacy of the trim and fill method in adjusting for these biases. The estimated effect sizes and the standard error were evaluated in terms of the statistical bias and the coverage probability. The results demonstrate that if publication bias is not adjusted it could lead to up to 40% bias in the treatment effect estimates. Utilization of the trim and fill method could reduce the bias in the overall estimate by more than half. The method is optimum in presence of moderate underlying bias but has minimal effects in presence of low and severe publication bias. Additionally, the trim and fill method improves the coverage probability by more than half when subjected to the same level of publication bias as those of the unadjusted data. The method however tends to produce false positive results and will incorrectly adjust the data for publication bias up to 45 % of the time. Nonetheless, the bias introduced into the estimates due to this adjustment is minimal

Keywords: Publication bias, Trim and Fill method, percentage relative bias, coverage probability

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Authors: Martín Agüero, Franco Madou, Gabriela Esperón, Daniela López De Luise

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This paper presents a software quality support tool, a Java source code evaluator and a code profiler based on computational intelligence techniques. It is Java prototype software developed by AI Group [1] from the Research Laboratories at Universidad de Palermo: an Intelligent Java Analyzer (in Spanish: Analizador Java Inteligente, AJI). It represents a new approach to evaluate and identify inaccurate source code usage and transitively, the software product itself. The aim of this project is to provide the software development industry with a new tool to increase software quality by extending the value of source code metrics through computational intelligence.

Keywords: Software metrics, artificial intelligence, neuralnetworks, clustering algorithms, expert systems

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Authors: J. E. Ortiz, P. Plaza, J. Herrero, I. Cabria, J. L. Blanco, J. Gavilanes, J. I. Escavy, I. López-Cilla, V. Yagüe, C. Pérez, S. Rodríguez, J. Rico, C. Serrano, J. Bernat

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The development of Artificial Intelligence services in mining processes, specifically in aggregate quarries, is facilitating automation and improving numerous aspects of operations. Ultimately, AI is transforming the mining industry by improving efficiency, safety and sustainability. With the ability to analyze large amounts of data and make autonomous decisions, AI offers great opportunities to optimize mining operations and maximize the economic and social benefits of this vital industry. Within the framework of the European DIGIECOQUARRY project, various services were developed for the identification of material quality, production estimation, detection of anomalies and prediction of consumption and production automatically with good results.

Keywords: Aggregates, artificial intelligence, automatization, mining operations.

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Authors: Andrus Pedai, Igor Astrov

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The focus of this paper is to compare common approaches for Systems of Innovation (SI) and identify proactive alternatives for driving the innovation. Proactive approaches will also consider short and medium term perspectives with developments in the field of Computer Technology and Artificial Intelligence. Concerning Computer Technology and Large Connected Information Systems, it is reasonable to predict that during current or the next century intelligence and innovation will be separated from the constraints of human driven management. After this happens, humans will be no longer driving the innovation and there is possibility that SI for new intelligent systems will set its own targets and exclude humans. Over long time scale these developments could result in scenario, which will lead to the development of larger, cross galactic (universal) proactive SI and Intelligence.

Keywords: Artificial intelligence, DARPA, Moore’s law, proactive innovation, singularity, systems of innovation.

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Precipitation forecast is important in avoid incident of natural disaster which can cause loss in involved area. This review paper involves three techniques from artificial intelligence namely logistic regression, decisions tree, and random forest which used in making precipitation forecast. These combination techniques through VAR model in finding advantages and strength for every technique in forecast process. Data contains variables from rain domain. Adaptation of artificial intelligence techniques involved on rain domain enables the process to be easier and systematic for precipitation forecast.

Keywords: Logistic regression, decisions tree, random forest, VAR model.

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Authors: Hadi Rouhi Belvirdi, Davoud Beheshtizadeh

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The increasing demand for sustainable construction materials has led to a growing interest in high-strength recycled concrete (HSRC). Utilizing recycled materials not only reduces waste but also minimizes the depletion of natural resources. This study explores the application of artificial intelligence (AI) techniques to model and predict the properties of HSRC. In the past two decades, the production levels in various industries and, consequently, the amount of waste have increased significantly. Continuing this trend will undoubtedly cause irreparable damage to the environment. For this reason, engineers have been constantly seeking practical solutions for recycling industrial waste in recent years. This research utilized the results of the compressive strength of 90-day HSRC. The method for creating recycled concrete involved replacing sand with crushed glass and using glass powder instead of cement. Subsequently, a feedforward artificial neural network was employed to model the compressive strength results for 90 days. The regression and error values obtained indicate that this network is suitable for modeling the compressive strength data.

Keywords: High-strength recycled concrete, feedforward artificial neural network, regression, artificial intelligence.

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Authors: Josefina Bengoechea, Alex Bell

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Higher education, with its brick-and-mortar facilities and credits-based on hours of study, was developed to serve the needs of a national, industrial, analogue economy. However, the ongoing process of globalization on the one hand, and the emergence of ever-changing needs of employers on the other hand, make this type of process-based education obsolete, and exclusive to students who can afford to pay a full-time tuition and dedicate 4 years of their lives exclusively to study. The creative destruction brought about by new technologies in the 21st century will not only reconfigure the labour market, as millions of jobs will be lost to Artificial Intelligence. The purpose of this paper is to consider if the implementation of technology is the solution to the problems faced in higher education. The paper builds upon a constructivist approach, combining a literature review and research on key publications.

Keywords: Artificial intelligence, employability, labour market, new technology in higher education.

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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The gases generated in oil filled transformers can be used for qualitative determination of incipient faults. The Dissolved Gas Analysis has been widely used by utilities throughout the world as the primarily diagnostic tool for transformer maintenance. In this paper, various Artificial Intelligence Techniques that have been used by the researchers in the past have been reviewed, some conclusions have been drawn and a sequential hybrid system has been proposed. The synergy of ANN and FIS can be a good solution for reliable results for predicting faults because one should not rely on a single technology when dealing with real–life applications.

Keywords: Dissolved Gas Analysis, Artificial IntelligenceTechniques, Incipient Faults, Transformer Fault Diagnosis, andHybrid Systems.

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Authors: Nhon Van Do

Abstract:

In the artificial intelligence field, knowledge representation and reasoning are important areas for intelligent systems, especially knowledge base systems and expert systems. Knowledge representation Methods has an important role in designing the systems. There have been many models for knowledge such as semantic networks, conceptual graphs, and neural networks. These models are useful tools to design intelligent systems. However, they are not suitable to represent knowledge in the domains of reality applications. In this paper, new models for knowledge representation called computational networks will be presented. They have been used in designing some knowledge base systems in education for solving problems such as the system that supports studying knowledge and solving analytic geometry problems, the program for studying and solving problems in Plane Geometry, the program for solving problems about alternating current in physics.

Keywords: Artificial intelligence, artificial intelligence and education, knowledge engineering, knowledge representation.

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Authors: A. Schuster

Abstract:

Many artificial intelligence (AI) techniques are inspired by problem-solving strategies found in nature. Robustness is a key feature in many natural systems. This paper studies robustness in artificial neural networks (ANNs) and proposes several novel, nature inspired ANN architectures. The paper includes encouraging results from experimental studies on these networks showing increased robustness.

Keywords: robustness, robust artificial neural networks architectures.

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Authors: Tarek Aboueldah, Hanan Farag

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Parallel Job Shop Scheduling Problem (JSSP) is a multi-objective and multi constrains NP-optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution. Thus, we propose a hybrid Artificial Intelligence (AI) model with Discrete Breeding Swarm (DBS) added to traditional AI to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization.

Keywords: Parallel Job Shop Scheduling Problem, Artificial Intelligence, Discrete Breeding Swarm, Car Sequencing and Operator Allocation, cost minimization.

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Authors: Ahmad Saiful Azlin Puteh Salin, Norlela Kamaludin, Siti Khadijah Ab Manan, Mohd Shatari Abdul Ghafar

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This paper discusses a new model of Islamic code of ethics for directors. Several corporate scandals and local (example Transmile and Megan Media) and overseas corporate (example Parmalat and Enron) collapses show that the current corporate governance and regulatory reform are unable to prevent these events from recurring. Arguably, the code of ethics for directors is under research and the current code of ethics only concentrates on binding the work of the employee of the organization as a whole, without specifically putting direct attention to the directors, the group of people responsible for the performance of the company. This study used a semi-structured interview survey of well-known Islamic scholars such as the Mufti to develop the model. It is expected that the outcome of the research is a comprehensive model of code of ethics based on the Islamic principles that can be applied and used by the company to construct a code of ethics for their directors.

Keywords: Code of ethics, director, Islam, ethics

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Authors: Murat Koklu, Yavuz Unal

Abstract:

Data mining can be called as a technique to extract information from data. It is the process of obtaining hidden information and then turning it into qualified knowledge by statistical and artificial intelligence technique. One of its application areas is medical area to form decision support systems for diagnosis just by inventing meaningful information from given medical data. In this study a decision support system for diagnosis of illness that make use of data mining and three different artificial intelligence classifier algorithms namely Multilayer Perceptron, Naive Bayes Classifier and J.48. Pima Indian dataset of UCI Machine Learning Repository was used. This dataset includes urinary and blood test results of 768 patients. These test results consist of 8 different feature vectors. Obtained classifying results were compared with the previous studies. The suggestions for future studies were presented.

Keywords: Artificial Intelligence, Classifiers, Data Mining, Diabetic Patients.

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Authors: P.-W. Tsai, J.-W. Chen, C.-W. Chen, C.-Y. Chen

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In recent years, many researchers are involved in the field of fuzzy theory. However, there are still a lot of issues to be resolved. Especially on topics related to controller design such as the field of robot, artificial intelligence, and nonlinear systems etc. Besides fuzzy theory, algorithms in swarm intelligence are also a popular field for the researchers. In this paper, a concept of utilizing one of the swarm intelligence method, which is called Bacterial-GA Foraging, to find the stabilized common P matrix for the fuzzy controller system is proposed. An example is given in in the paper, as well.

Keywords: Half-circle fuzzy numbers, predictions, swarm intelligence, Lyapunov method.

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Authors: Maamar Ali Saud Al Tobi, Geraint Bevan, K. P. Ramachandran, Peter Wallace, David Harrison

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Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.

Keywords: Centrifugal pump setup, vibration analysis, artificial intelligence, genetic algorithm.

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Authors: Zeljko Panian

Abstract:

Through the course of this paper we define Locationbased Intelligence (LBI) which is outgrowing from process of amalgamation of geolocation and Business Intelligence. Amalgamating geolocation with traditional Business Intelligence (BI) results in a new dimension of BI named Location-based Intelligence. LBI is defined as leveraging unified location information for business intelligence. Collectively, enterprises can transform location data into business intelligence applications that will benefit all aspects of the enterprise. Expectations from this new dimension of business intelligence are great and its future is obviously bright.

Keywords: Business intelligence, geolocation, location-based intelligence, innovation, location-intelligent business

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Authors: S. B. Tende, H. L. Abubakar

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It is acknowledged that small and medium enterprises (SMEs) may encounter different ethical issues and pressures that could affect the way in which they strategize or make decisions concerning the outcome of their business. Therefore, this research aimed at assessing entrepreneurial ethics in the business of SMEs in Nigeria. Secondary data were adopted as source of corpus for the analysis. The findings conclude that a sound entrepreneurial ethics system has a significant effect on the level of performance of SMEs in Nigeria. The Nigerian Government needs to provide both guiding and physical structures; as well as learning systems that could inculcate these entrepreneurial ethics.

Keywords: Entrepreneurial ethics, culture, performance, SME.

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Authors: Iman Hajirad

Abstract:

Water, as a vital element for human survival and natural ecosystems, has become one of the most pressing challenges in today’s world. Various crises, including diminishing water resources, climate change, and population growth, have made water resource management more critical than ever. In this context, artificial intelligence (AI), as an innovative technology, can play a pivotal role in optimizing water consumption, predicting water crises, and managing resources effectively. Leveraging big data, machine learning, the Internet of Things (IoT), and remote sensing, AI has significantly contributed to drought and flood prediction, agricultural irrigation optimization, and water quality management. This paper explores the applications of AI in water resource management, including water resource prediction and modeling, agricultural water use optimization, pollution control, and crisis management. The findings indicate that the implementation of AI technologies can enhance water resource management, reduce water waste, and preserve water quality.

Keywords: Water crisis, water resource management, water planning, artificial intelligence.

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Authors: Amir Hossein Davami, Ali Gholami, Ebrahim Panahpour

Abstract:

Although, it is a long time that human know about the importance of environment in life, but at the last decade of 20 century, the space that was full of hot scientific, collegial and political were made in environmental challenge, So much that, this problem not only disarrange the peace and security of life, but also it has threatened human existence. One of the problems in last years that are significant for authorities is unsatisfactory achieved results against of using huge cost for magnificent environmental projects. This subject leads thinker to this thought that for solving the environmental problems it is needed new methods include of sociology, ethics and philosophic, etc. methods apart of technical affairs. Environment ethics is a new branch of philosophic ethics discussion that discusses about the ethics relationship between humans and universe that is around them. By notifying to the above considered affairs, in today world, necessity of environmental ethics for environment management is reduplicated. In the following the article has been focused on environmental ethics role and environmental management methods and techniques for developing it.

Keywords: Environmental ethics and philosophy, Environmental challenges, Management techniques, Ethical values.

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Authors: Z. M. Najmi

Abstract:

Over the years, the question around Artificial Intelligence has always been one with many answers. Whether by means of use in business and industry or complicated algorithmic programming, management of these technologies has always been the core focus. More recently, technologies have been questioned in industry and society alike as to whether they have improved human-centred design, assisted choices and objectives, and had a hand in systematic processes across the board. With these questions the answer may lie within AI technologies, and the steps needed in removing common human error. Elements such as Machine Learning, Deep Learning, Recommender Systems and Natural Language Processing will all be features to consider moving forward. Our previous intervention with AI applications has resulted in increased productivity, however, raised concerns for the continuation of traditional human-centred occupations. Emerging technologies such as Augmented Reality and Virtual Reality have all played a part in this during AI’s prominent rise. As mentioned, AI has been constantly under the microscope; the benefits and drawbacks may seem endless is wide, but AI is something we must take notice of and adapt into our everyday lives. The aim of this paper is to give an overview of the technologies surrounding A.I. and its’ related technologies. A comprehensive review has been written as a timeline of the developing events and key points in the history of Artificial Intelligence. This research is gathered entirely from secondary research, academic statements of knowledge and gathered to produce an understanding of the timeline of AI.

Keywords: Artificial Intelligence, Deep Learning, Augmented Reality, Reinforcement Learning, Machine Learning, Supervised Learning.

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Authors: R. N. Patel, S. K. Sinha, R. Prasad

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In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuzzy logic controller (acting as the secondary controller in the AGC system) has been designed. The controller gives an improved dynamic performance for both hydrothermal and thermal-thermal power systems under a variety of operating conditions.

Keywords: Artificial intelligence, Automatic generation control, Fuzzy control, Genetic Algorithm, Particle swarm optimization, Power systems.

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