Explainable Artificial Intelligence (XAI) Approaches for Transparency and Accountability in Financial Decision-Making

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

Recently, there has been a growing trend in incorporating Artificial Intelligence (AI) into financial decision-making, prompting concerns about the transparency and accountability of these intricate systems. This study investigates impact Explainable (XAI) approaches alleviating improving decision-making processes. The paper commences by outlining current landscape AI applications finance, underscoring complex opaque nature advanced machine learning models. lack interpretability models presents significant challenge, as stakeholders, regulators, end-users often struggle to comprehend reasoning behind AI-driven decisions. opacity raises questions regarding trust, particularly critical scenarios. primary focus research centers on analysis implementation XAI techniques introduce Various methods, including rule-based systems, model-agnostic approaches, interpretable models, are scrutinized for their effectiveness producing understandable explanations explores how can be tailored meet distinct requirements domain, where is essential regulatory compliance stakeholder confidence. Moreover, delves potential mechanisms within institutions. By offering model outputs, not only enhances but also empowers professionals identify rectify biases, errors, or unethical behaviour algorithms. promoting accountability, addresses ethical facilitates responsible trustworthy deployment sector. This, turn, contributes advancement fair, reliable, secure

Language: Английский

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Integrating Building Information Modelling (BIM) with ChatGPT, Bard, and similar generative artificial intelligence in the architecture, engineering, and construction industry: applications, a novel framework, challenges, and future scope

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

The architecture, engineering, and construction (AEC) sector is currently experiencing a profound shift in its paradigm through the incorporation of Building Information Modelling (BIM) advanced generative artificial intelligence (AI) technologies. This study delves into diverse applications this transformative integration, presenting pioneering framework for merging BIM with models like ChatGPT Bard. integrated approach outlined research paper has broad implications. Initially, it explores augmentation design process, where AI enhances creative input architects engineers by generating innovative alternatives rooted data. BIM's collaborative ethos extended natural language interfaces from ChatGPT, fostering seamless communication idea exchange among project stakeholders. In phase, integration streamlines real-time decision-making on-site personnel, providing AI-generated insights based on ensures heightened efficiency, cost-effectiveness, risk management. synergy between also harnessed simulation analysis, enabling predictions related to structural performance, energy environmental impact. introduces an seamlessly AI, prioritizing interoperability, data consistency, user-friendly interfaces. Designed adapt dynamic nature AEC projects, promotes continuous collaboration information exchange. It establishes standardized platform harnessing strengths both technologies, ensuring cohesive efficient workflow across lifecycle. Nevertheless, brings forth challenges, including security, ethical considerations, demand extensive computational resources. provides foundational upcoming studies industry practices, paving way more intelligent, collaborative, ecosystem.

Language: Английский

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YOLO and Faster R-CNN object detection for smart Industry 4.0 and Industry 5.0: applications, challenges, and opportunities

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

The rise of Industry 4.0 and the emerging paradigm 5.0 have driven unprecedented technological progress in various fields. Central to this transformation are real-time object detection technologies, notably You Only Look Once (YOLO) Faster Region Convolutional Neural Network (Faster R-CNN) algorithms. This study thoroughly examines applications, challenges, prospects YOLO R-CNN diverse industrial domains. In realm automation, these algorithms redefined efficiency safety standards by enabling rapid precise recognition, thus enhancing overall production workflows. Furthermore, construction industry has experienced significant advancements project management site safety, thanks accurate identification materials equipment. healthcare, revolutionized patient care facilitating medical instruments anomalies, thereby improving diagnostics treatment processes. integration into autonomous vehicles substantially enhanced their capabilities, ensuring superior road navigation. Additionally, precision agriculture, streamlined crop management, leading increased agricultural productivity sustainability. Moreover, retail e-commerce sectors undergone a shift with personalized customer experiences efficient inventory all powered technologies. Despite remarkable advancements, paper explores challenges such as data privacy concerns, computational complexity, ethical considerations. Addressing opens unique avenues for further research innovation. Lastly, environmental monitoring also benefited from algorithms, tracking analysis changes informed decision-making towards sustainable future. illuminates transformative potential detection, paving way ongoing upcoming 5.0. These technologies shaping smarter, more connected, future across sectors.

Language: Английский

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Embryonic Machine-Deep Learning, Smart Healthcare and Privacy Deliberations in Hospital Industry: Lensing Confidentiality of Patient’s Information and Personal Data in Legal-Ethical Landscapes Projecting Futuristic Dimensions

Published: Jan. 1, 2024

Language: Английский

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TACKLING ECONOMIC INEQUALITIES THROUGH BUSINESS ANALYTICS: A LITERATURE REVIEW

Computer Science & IT Research Journal, Journal Year: 2024, Volume and Issue: 5(1), P. 60 - 80

Published: Jan. 9, 2024

This literature review delves into the intersection of business analytics and its potential to address economic inequalities. In an era where data-driven decision-making is becoming ubiquitous, this study explores how organizations leverage analyze, understand, mitigate disparities. The encompasses a diverse range scholarly articles, research papers, case studies, providing insights strategies, methodologies, impact utilizing tackle It transitions exploration role that plays in context, emphasizing power inform influence decision-makers various sectors. Examining predictive modeling techniques within contribute identifying patterns trends strategic aimed at reducing Investigating applied formulate assess effectiveness policies designed disparities, with focus on evidence-based decision-making. Analyzing studies showcase businesses adopt more inclusive practices areas such as hiring, promotions, supply chain management, contributing reduction ethical dimensions employing pursuit inequalities, including issues related privacy, consent, bias mitigation. concludes synthesis findings, gaps current proposing avenues for future exploration. By synthesizing perspectives, empirical evidence, contributes comprehensive understanding can serve powerful tool collective effort inequalities global scale. Keywords: Business Analytics, Inequalities, Bias Mitigation, Technological Advancement, Review.

Language: Английский

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Metaverse as a cutting-edge platform for attaining Sustainable Development Goals (SDGs)

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

The Metaverse, an immersive virtual reality realm where users engage with computer-generated environments, is swiftly emerging as a transformative platform poised to address and propel Sustainable Development Goals (SDGs). This research delves into the intersections between Metaverse SDGs, shedding light on how this cutting-edge technology can be harnessed sustainable development across diverse sectors. Commencing comprehensive overview, paper details evolution, key components, capabilities of Metaverse. Underscoring its dynamic interactive essence, emphasizes potential revolutionize conventional approaches education, healthcare, environmental conservation, economic empowerment. A thorough analysis unveils Metaverse's role in democratizing education by transcending geographical boundaries providing inclusive learning environments. nature facilitates experiential learning, enhancing accessibility, fostering global collaboration fulfill SDG 4 (Quality Education). Additionally, explores contribute healthcare solutions through medical consultations, training professionals, simulating scenarios. innovative approach holds disparities, advance 3 (Good Health Well-being), improve overall public health. Environmental sustainability takes center stage serves for raising awareness about climate change, promoting practices, eco-friendly solutions. aligns 13 (Climate Action) 15 (Life Land), emphasizing capacity inspire real-world stewardship. Economic empowerment addressed discussions economies within highlighting opportunities entrepreneurship, job creation, financial inclusion. exploration corresponds 1 (No Poverty), 8 (Decent Work Growth), 10 (Reduced Inequality).

Language: Английский

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Leading-edge Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and Internet of Things (IoT) technologies for enhanced wastewater treatment systems

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

The escalating global demand for clean water mandates the development of inventive approaches to wastewater treatment systems. This paper investigates incorporation cutting-edge technologies, specifically Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and Internet Things (IoT), revolutionize elevate procedures. collaborative application these technologies presents a promising avenue optimizing efficiency, sustainability, overall performance within infrastructure. play pivotal roles in predictive modeling decision-making processes plants. These facilitate real-time monitoring quality parameters, enabling dynamic adjustments protocols based on data-driven insights. adaptive nature AI ML algorithms enhances system resilience, diminishes operational costs, ensures adherence rigorous environmental standards. integration Blockchain technology introduces decentralized secure framework managing data By capitalizing inherent transparency immutability blockchain, stakeholders can trace complete lifecycle treatment, from source discharge. not only promotes accountability but also nurtures trust among regulators, utilities, public, fostering more transparent sustainable management ecosystem. Moreover, contributes establishment connected responsive Sensor networks embedded throughout process enable collection, facilitating remote control. seamless communication between IoT devices prompt identification anomalies potential failures, allowing timely intervention averting hazards. research underscores emphasizes significance their address evolving challenges contribute future.

Language: Английский

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Explainable Artificial Intelligence (XAI) in healthcare: Interpretable Models for Clinical Decision Support

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

In healthcare, the incorporation of Artificial Intelligence (AI) plays a pivotal role in enhancing diagnostic precision and guiding treatment decisions. Nevertheless, lack transparency conventional AI models poses challenges gaining trust clinicians comprehending rationale behind their This research paper explores Explainable (XAI) its application with specific focus on transparent designed for clinical decision support various medical disciplines. The initiates by underscoring crucial requirement interpretability systems within healthcare realm. Recognizing diverse nature specialties, study investigates tailored XAI approaches to meet distinctive needs areas such as radiology, pathology, cardiology, oncology. Through thorough review existing literature analysis, identifies key obstacles prospects implementing across varied contexts. field cornerstone imaging, proves beneficial elucidating decision-making procedures image analysis algorithms. probes into impact interpretable radiological diagnoses, examining how can seamlessly integrate AI-generated insights workflows. Within where is utmost importance, clarifies enhance histopathological assessments. By demystifying intricacies AI-driven pathology models, aims empower pathologists leverage these tools more accurate diagnoses. Cardiology, characterized complex interplay physiological parameters, benefits from offering intelligible explanations cardiovascular risk predictions recommendations. delves highlighting potential systems. Moreover, oncology, decisions hinge precise identification characterization tumors, aids unraveling intricate machine learning models. This, turn, fosters among oncologists utilizing personalized strategies.

Language: Английский

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Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in building and construction industry: applications, framework, challenges, and future scope

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

The infusion of generative artificial intelligence (AI), as exemplified by models such ChatGPT and Bard is proving to be a revolutionary catalyst within the building construction sector. This exploration delves into myriad applications, establishes conceptual framework, confronts challenges, delineates prospective trajectory harnessing AI across diverse stages lifecycle. In domain project management scheduling, contribute optimal resource allocation, task sequencing, timeline optimization, thereby elevating overall efficiency delivery. Design optimization equally pivotal, assists architects engineers in crafting innovative designs that concurrently adhere functional aesthetic criteria. predictive prowess fortifies risk management, furnishing stakeholders with insights potential risks effective mitigation strategies. Meanwhile, realm cost estimation budgeting, enhanced accuracy speed offered optimize financial planning allocation. Supply chain benefits from streamlined processes driven insights, ensuring timely cost-effective procurement materials. Generative linchpin quality control, identifying defects deviations standards enhance quality. Real-time data analysis strengthens site monitoring safety protocols, enabling proactive secure working environment. Collaboration communication teams are augmented AI, facilitating seamless information exchange decision-making processes. Predictive maintenance asset undergo transformation, algorithms predicting equipment failures optimizing schedules. Furthermore, integration tackles imperative energy sustainability Models like bard significantly for conservation sustainable practices. paper also explores incorporation reality (AR), virtual (VR), Building Information Modeling (BIM). Ethical concerns, privacy, robust cybersecurity measures necessitate careful consideration. As industry embraces these innovations, substantial improvements efficiency, sustainability, outcomes poised unfold.

Language: Английский

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Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Aug. 23, 2024

This paper investigates the application of three nature-inspired optimisation algorithms – SHO, MFO, and GOA combined with four machine learning methods Gaussian Processes, Linear Regression, MLP, Random Forest to enhance carbon dioxide emission prediction in OECD Asia Oceania region. The study uses historical emissions data, socioeconomic indicators such as GDP, population density, energy consumption, urbanisation rates, environmental temperature, precipitation, forest cover. Through comprehensive experimentation, evaluates performance each combination, revealing varying effectiveness levels. MFO-MLP combination achieved highest accuracy R2 values 0.9996 0.9995 RMSE 11.7065 12.8890 for training testing datasets, respectively. GOA-MLP configuration 0.9994 0.99934 15.01306 14.59333. SHO-MLP while effective, showed lower 0.9915 0.9946 55.4516 41.575. findings suggest hybrid techniques can significantly compared conventional methods. research provides valuable insights policymakers stakeholders, indicating that optimised models support more informed effective policy-making sustainability efforts Future should explore additional ensemble improve robustness accuracy. These offer a robust tool forecast accurately, aiding developing targeted strategies reduce footprints achieve climate goals.

Language: Английский

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