Results 141 to 150 of about 55,142 (282)

The role of IoT and XAI convergence in the prediction, explanation, and decision of customer perceived value (CPV) in SMEs: a theoretical framework and research proposition perspective

Discover Internet of Things
The goal of this study is to look at how the convergence of IoT and XAI (IoT-XAI) effects the explanation, prediction, and decision-making on customer perceived value (CPV) in SMEs, utilising CPV and IoT-XAI convergence theories.
Kwabena Abrokwah-Larbi
doaj   +1 more source

Understanding and Mitigating Bias From Artificial Intelligence in Otolaryngology: A State‐of‐the‐Art Review

World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.
ABSTRACT Objective To provide an overview of potential biases resulting from the utilization of artificial intelligence (AI) in otolaryngology and techniques to mitigate them. Data Sources Literature review and expert opinion. Conclusions AI promises to fundamentally transform medicine.
Matthew T. Ryan, David A. Gudis
wiley   +1 more source

Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions

World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.
ABSTRACT Objective To provide a comprehensive review of the current landscape of artificial intelligence (AI) applications in voice disorder, with emphasis on emerging applications, limitations, and future directions for clinical integration. Methods Literature review.
Rachel B. Kutler, Anaïs Rameau
wiley   +1 more source

Exploration and practice of XAI architecture

大数据
XAI(explainable AI) is an important component of trusted AI.In-depth research on the technology points of XAI has been carried out in the current industry, but systematic research on engineering implementation is lacking.This paper proposed a general XAI
Zhengxun XIA, Jianfei TANG, Yifan YANG, Shengmei LUO, Yan ZHANG, Fenglei TAN, Shengru TAN   +6 more
doaj  

Developing Predictive and Explainable Models for Cryptocurrency Delistings: A Case Study of Binance Exchange

Asia-Pacific Journal of Financial Studies, EarlyView.
Abstract This study develops an explainable machine learning model to predict cryptocurrency delistings using Binance data. It combines quantitative indicators (price, volume) with qualitative data from real‐time news and Reddit. Latent Dirichlet Allocation (LDA) is used to extract topic trends and community reactions, which are transformed into time ...
Sungju Yang, Hunyeong Kwon
wiley   +1 more source

What if Adam Smith Debated an AI Economist: A Thought Experiment on Markets, Ethics, and the Invisible Hand

Business Ethics, the Environment &Responsibility, EarlyView.
ABSTRACT Can AI‐driven capitalism sustain the moral preconditions of market order? We stage a dialogue between Adam Smith and a steel‐manned “EconAI” to test four Moral‐Market‐Fitness criteria: trustworthiness, fairness, non‐domination, and contestability, across 11 dilemmas.
Alexandra‐Codruța Bîzoi, Cristian‐Gabriel Bîzoi   +1 more
wiley   +1 more source

XAI In Fraud Detection: A Causal Perspective

Abstract Fraud detection systems powered by machine learning (ML) often lack transparency, raising concerns about trustworthiness and interpretability. While Explainable AI (XAI) addresses these issues, many methods rely on correlation rather than causation, potentially overlooking true fraud patterns.
van Veen, Katiuscka, Ahmed, Faizan, van Keulen, Maurice   +2 more
openaire   +1 more source

Using multilabel classification neural network to detect intersectional DIF with small sample sizes

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract This study introduces InterDIFNet, a multilabel classification neural network for detecting intersectional differential item functioning (DIF) in educational and psychological assessments, with a focus on small sample sizes. Unlike traditional marginal DIF methods, which often fail to capture the effects of intersecting identities and require ...
Yale Quan, Chun Wang
wiley   +1 more source

L‐VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts

Computer Graphics Forum, EarlyView.
L‐VISP is a human‐machine solution that uses visual analytics for LSTM modelling in clinical research. L‐VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context.
C. Floricel, Y. Wang, A. Wentzel, C. D. Fuller, G. E. Marai, M. E. Papka, G. Canahuate   +6 more
wiley   +1 more source

A Mechanistic Explanatory Strategy for XAI

CoRR
Despite significant advancements in XAI, scholars continue to note a persistent lack of robust conceptual foundations and integration with broader discourse on scientific explanation. In response, emerging XAI research increasingly draws on explanatory strategies from various scientific disciplines and the philosophy of science to address these gaps ...
openaire   +2 more sources