Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 240, P. 122442 - 122442

Published: Nov. 16, 2023

Language: Английский

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Multimodal data fusion for cancer biomarker discovery with deep learning

Nature Machine Intelligence, Journal Year: 2023, Volume and Issue: 5(4), P. 351 - 362

Published: April 6, 2023

Language: Английский

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Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators

Published: July 17, 2023

Large language models that exhibit instruction-following behaviour represent one of the biggest recent upheavals in conversational interfaces, a trend large part fuelled by release OpenAI's ChatGPT, proprietary model for text generation fine-tuned through reinforcement learning from human feedback (LLM+RLHF). We review risks relying on software and survey first crop open-source projects comparable architecture functionality. The main contribution this paper is to show openness differentiated, offer scientific documentation degrees fast-moving field. evaluate terms code, training data, weights, RLHF licensing, documentation, access methods. find while there fast-growing list billing themselves as 'open source', many inherit undocumented data dubious legality, few share all-important instruction-tuning (a key site where annotation labour involved), careful exceedingly rare. Degrees are relevant fairness accountability at all points, collection curation architecture, fine-tuning deployment.

Language: Английский

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A review of uncertainty estimation and its application in medical imaging

Meta-Radiology, Journal Year: 2023, Volume and Issue: 1(1), P. 100003 - 100003

Published: June 1, 2023

The use of AI systems in healthcare for the early screening diseases is great clinical importance. Deep learning has shown promise medical imaging, but reliability and trustworthiness limit their deployment real scenes, where patient safety at stake. Uncertainty estimation plays a pivotal role producing confidence evaluation along with prediction deep model. This particularly important uncertainty model's predictions can be used to identify areas concern or provide additional information clinician. In this paper, we review various types learning, including aleatoric epistemic uncertainty. We further discuss how they estimated imaging. More importantly, recent advances models that incorporate Finally, challenges future directions hope will ignite interest community researchers an up-to-date reference regarding applications

Language: Английский

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Fault Detection for Point Machines: A Review, Challenges, and Perspectives

Actuators, Journal Year: 2023, Volume and Issue: 12(10), P. 391 - 391

Published: Oct. 18, 2023

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, safe reliable behavior of point pivotal rail transportation. Recently, scholars researchers have attempted deploy various kinds sensors on anomaly detection and/or incipient fault using date-driven algorithms. However, challenges arise when deploying condition monitoring trackside in practical applications. This article begins by reviewing studies machines, encompassing employed methods evaluation metrics. It subsequently conducts an in-depth analysis outlines envisioned intelligent system. Finally, it presents eight promising research directions along with a blueprint machine detection.

Language: Английский

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PRIVACY LAW CHALLENGES IN THE DIGITAL AGE: A GLOBAL REVIEW OF LEGISLATION AND ENFORCEMENT

International Journal of Applied Research in Social Sciences, Journal Year: 2024, Volume and Issue: 6(1), P. 73 - 88

Published: Jan. 25, 2024

As the world becomes increasingly interconnected through digital technologies, protection of individuals' privacy has emerged as a critical concern. This paper conducts comprehensive global review legislation and enforcement mechanisms, shedding light on challenges posed by age. With focus intricate balance between technological advancements fundamental right to privacy, study explores evolving legal landscape its implications for individuals, businesses, governments. The analysis encompasses diverse jurisdictions, highlighting variations in laws approaches across regions. From European Union's robust General Data Protection Regulation (GDPR) nuanced Asia Americas, this synthesizes regulatory frameworks. Special attention is given emerging issues such use artificial intelligence, biometrics, surveillance which pose unique existing paradigms. Moreover, investigates effectiveness mechanisms ensuring compliance with laws. It examines role governmental agencies, bodies, international collaborations addressing cross-border data flows challenges. also evaluates impact recent high-profile incidents shaping legislative responses strategies. By presenting holistic view law age, research contributes ongoing discourse safeguarding rights an era rapid innovation. findings provide valuable insights policymakers, practitioners, individuals seeking deeper understanding dynamics surrounding scale. Keywords: Law, Privacy Digital Age, Review, Protection.

Language: Английский

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A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons

Communications Medicine, Journal Year: 2023, Volume and Issue: 3(1)

Published: March 30, 2023

Surgeons who receive reliable feedback on their performance quickly master the skills necessary for surgery. Such performance-based can be provided by a recently-developed artificial intelligence (AI) system that assesses surgeon's based surgical video while simultaneously highlighting aspects of most pertinent to assessment. However, it remains an open question whether these highlights, or explanations, are equally all surgeons.Here, we systematically quantify reliability AI-based explanations videos from three hospitals across two continents comparing them generated humans experts. To improve propose strategy training with -TWIX -which uses human as supervision explicitly teach AI highlight important frames.We show often align they not different sub-cohorts surgeons (e.g., novices vs. experts), phenomenon refer explanation bias. We also TWIX enhances mitigates bias, and improves systems hospitals. These findings extend environment where medical students today.Our study informs impending implementation AI-augmented surgeon credentialing programs, contributes safe fair democratization surgery.Surgeons aim One such skill is suturing which involves connecting objects together through series stitches. Mastering improved providing quality performance. absent practice. Although provided, in theory, use computational model assess surgeon’s skill, this unknown. Here, compare experts demonstrate overlap one another. teaching further new Our outline potential support focused particular guide programs give qualifications complementing assessments increase trustworthiness assessments.

Language: Английский

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Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

Water Research, Journal Year: 2024, Volume and Issue: 255, P. 121499 - 121499

Published: March 20, 2024

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that model produced inconsistent results due to data outliers, which significantly impact reliability accuracy. The present study was carried out assess of outliers on recently developed Irish Water Quality Index (IEWQI) model, relies techniques. To author's best knowledge, no systematic framework for evaluating influence such models. For purposes assessing outlier (WQ) this first initiative research introduce comprehensive approach combines with advanced statistical proposed implemented Cork Harbour, Ireland, evaluate IEWQI model's sensitivity input indicators quality. In order detect outlier, utilized two widely used ML techniques, including Isolation Forest (IF) Kernel Density Estimation (KDE) within dataset, predicting WQ without these outliers. validating results, five commonly measures. performance metric (R2) indicates improved slightly (R2 increased from 0.92 0.95) after removing input. But scores were statistically differences among actual values, predictions 95% confidence interval at p < 0.05. uncertainty also contributed <1% final assessment using both datasets (with outliers). addition, all measures indicated techniques provided reliable can be detecting their impacts model. findings reveal although had architecture, they moderate rating schemes' This finding could improve accuracy as well helpful mitigating eclipsing problem. provide evidence how influenced reliability, particularly since confirmed effective accurately despite presence It occur spatio-temporal variability inherent indicators. However, assesses underscores important areas future investigation. These include expanding temporal analysis multi-year data, examining spatial patterns, detection methods. Moreover, it is essential explore real-world revised categories, involve stakeholders management, fine-tune parameters. Analysing across varying resolutions incorporating additional environmental enhance assessment. Consequently, offers valuable insights strengthen robustness provides avenues enhancing its utility broader applications. successfully adopted affect current Harbour only single year data. should tested various domains response terms resolution domain. Nevertheless, recommended conducted adjust or revise schemes investigate practical effects updated categories. potential recommendations adaptability reveals effectiveness applicability more general scenarios.

Language: Английский

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Higher Education’s Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania

Journal of University Teaching and Learning Practice, Journal Year: 2024, Volume and Issue: 21(06)

Published: April 19, 2024

Higher education is currently under a significant transformation due to the emergence of generative artificial intelligence (GenAI) technologies, hype surrounding GenAI and increasing influence educational technology business groups over tertiary education. This commentary, prepared for Special Issue Journal University Teaching & Learning Practice (JUTLP) on “Enhancing student engagement using Artificial Intelligence (AI) chatbots,” delves into complex landscape opportunities threats that AI chatbots, including ChatGPT, introduce realm higher We argue while offers promise in enhancing pedagogy, research, administration, support, concerns around academic integrity, labour displacement, embedded biases, environmental sustainability, increased commercialisation, regulatory gaps necessitate critical approach. Our commentary advocates development literacy among educators students, emphasising necessity foster an environment responsible innovation informed use AI. posit successful integration must be grounded principles ethics, equity, prioritisation aims human values. By offering nuanced exploration these issues, our contribute ongoing discourse how institutions can navigate rise GenAI, ensuring technological advancements benefit all stakeholders upholding core

Language: Английский

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Automated data processing and feature engineering for deep learning and big data applications: A survey

Journal of Information and Intelligence, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Modern approach to artificial intelligence (AI) aims design algorithms that learn directly from data. This has achieved impressive results and contributed significantly the progress of AI, particularly in sphere supervised deep learning. It also simplified machine learning systems as process is highly automated. However, not all data processing tasks conventional pipelines have been In most cases be manually collected, preprocessed further extended through augmentation before they can effective for training. Recently, special techniques automating these emerged. The automation driven by need utilize large volumes complex, heterogeneous big applications. Today, end-to-end automated based on (AutoML) are capable taking raw transforming them into useful features Big Data intermediate stages. this work, we present a thorough review approaches pipelines, including preprocessing– e.g., cleaning, labeling, missing imputation, categorical encoding–as well (including synthetic generation using generative AI methods) feature engineering–specifically, extraction, construction selection. addition specific tasks, discuss use AutoML methods tools simultaneously optimize stages pipeline.

Language: Английский

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