Results 111 to 120 of about 23,034 (235)

A Unified Fuzzy–Explainable AI Framework (FAS-XAI) for Customer Service Value Prediction and Strategic Decision-Making

AI
Real-world decision-making often involves uncertainty, incomplete data, and the need to evaluate alternatives based on both quantitative and qualitative criteria.
Gabriel Marín Díaz
doaj   +1 more source

A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention

Obesity Reviews, EarlyView.
ABSTRACT This systematic review examines the applications of artificial intelligence (AI) in preventing obesity, addressing a critical public health issue that affects a substantial portion of the population. With obesity rates rising alarmingly, particularly in the United States, this review synthesizes findings from 46 studies published between 2008 ...
Atefehsadat Haghighathoseini, Shuo‐yu Lin, Ge Song, Ruopeng An, Hong Xue   +4 more
wiley   +1 more source

Enhancing explainability in pacu fish image segmentation using saliency maps and combined explainable AI methods

Smart Agricultural Technology
Advances in Artificial Intelligence (AI) have sparked concerns regarding the transparency of model outputs, necessitating the development of eXplainable Artificial Intelligence (XAI) techniques.
Juliana da C. Feitosa, Fabrício M. Batista, Juliana C.F. Catharino, Milena V. Freitas, Diogo T. Hashimoto, João Paulo Papa, José Remo F. Brega   +6 more
doaj   +1 more source

AI Alignment Versus AI Ethical Treatment: 10 Challenges

Analytic Philosophy, EarlyView.
ABSTRACT A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding ...
Adam Bradley, Bradford Saad
wiley   +1 more source

Open and Extensible Benchmark for Explainable Artificial Intelligence Methods

Algorithms
The interpretability requirement is one of the largest obstacles when deploying machine learning models in various practical fields. Methods of eXplainable Artificial Intelligence (XAI) address those issues.
Ilia Moiseev, Ksenia Balabaeva, Sergey Kovalchuk   +2 more
doaj   +1 more source

Personalized Model‐Driven Interventions for Decisions From Experience

Topics in Cognitive Science, EarlyView.
Abstract Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance‐based learning ...
Edward A. Cranford, Christian Lebiere, Cleotilde Gonzalez, Palvi Aggarwal, Sterling Somers, Konstantinos Mitsopoulos, Milind Tambe   +6 more
wiley   +1 more source

Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine. [PDF]

Online J Public Health Inform, 2023
Gniadek T, Kang J, Theparee T, Krive J.
europepmc   +1 more source

Modeling and generating user‐centered contrastive explanations for the workforce scheduling and routing problem

International Transactions in Operational Research, Volume 33, Issue 3, Page 1525-1558, May 2026.
Abstract In the last decade, explainability has been attracting much attention in the machine learning community. However, this research topic extends beyond this field to encompass others such as operations research and combinatorial optimization (CO).
Mathieu Lerouge, Céline Gicquel, Vincent Mousseau, Wassila Ouerdane   +3 more
wiley   +1 more source

Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions

IEEE Access
Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous vehicles (AVs) promises safer and more ecologically friendly ...
Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel   +3 more
doaj   +1 more source

Beauty is in the explainable artificial intelligence (XAI) of the "agnostic" beholder. [PDF]

Transl Cancer Res, 2023
Laios A, De Jong D, Kalampokis E.
europepmc   +1 more source