Results 31 to 40 of about 36,247 (313)

What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach

Bioengineering
This article reflects on explainability in the context of medical artificial intelligence (AI) applications, focusing on AI-based clinical decision support systems (CDSS).
Elisabeth Hildt
doaj   +1 more source

Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs

Diagnostics, 2022
To identify the best transfer learning approach for the identification of the most frequent abnormalities on chest radiographs (CXRs), we used embeddings extracted from pretrained convolutional neural networks (CNNs).
Noemi Gozzi, Edoardo Giacomello, Martina Sollini, Margarita Kirienko, Angela Ammirabile, Pierluca Lanzi, Daniele Loiacono, Arturo Chiti   +7 more
doaj   +1 more source

An Approach to Task Representation Based on Object Features and Affordances

Sensors, 2022
Multi-purpose service robots must execute their tasks reliably in different situations, as well as learn from humans and explain their plans to them.
Paul Gajewski, Bipin Indurkhya
doaj   +1 more source

Combining Human-centred Explainability and Explainable AI [PDF]

, 2022
International audienceThis position paper looks at differences between the current understandings of human-centered explainability and explainability AI. We discuss current ideas in both fields, as well as the differences and opportunities we discovered.
Koch, Janin, Fortes, Vitor
core  

Assessing Explainability in Reinforcement Learning

, 2021
Reinforcement Learning performs well in many different application domains and is starting to receive greater authority and trust from its users. But most people are unfamiliar with how AIs make their decisions and many of them feel anxious about AI ...
Zelvelder, Amber,, Westberg, Marcus,, Kary Främling, Marcus Westberg, Amber E. Zelvelder, Främling, Kary,   +5 more
core   +1 more source

X-OODM: Explainable Object-Oriented Design Methodology

IEEE Access
In software applications and decision-making systems, the explainability features can be instrumental for explicating internal working, accountability, understanding, fairness, and interpretation of decisions, processes, and data.
Abqa Javed, Muhammad Shoaib, Abdul Jaleel   +2 more
doaj   +1 more source

Machine Learning in Ratemaking, an Application in Commercial Auto Insurance

Risks, 2022
This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy ...
Spencer Matthews, Brian Hartman
doaj   +1 more source

The role of explainability throughout the MLOps lifecycle: review and research agenda

Frontiers in Computer Science
As Machine Learning Operations (MLOps) adoption accelerates, systematic integration of explainability is imperative for reliability, transparency, and continuous quality assurance.
Sule Tekkesinoglu, Matthias Wagner, Per Runeson   +2 more
doaj   +1 more source

Sequence-Based Explainable Hybrid Song Recommendation

Frontiers in Big Data, 2021
Despite advances in deep learning methods for song recommendation, most existing methods do not take advantage of the sequential nature of song content.
Khalil Damak, Olfa Nasraoui, William Scott Sanders   +2 more
doaj   +1 more source

Explaining visual counterfactual explainers

Trans. Mach. Learn. Res., 2023
Altres ajuts: this work was supported by the Generalitat de Catalunya under the Industrial Doctorate Program (grant number 2020DI62).
Velazquez Dorta, Diego Alejandro, Rodríguez López, Pau, Lacoste, Alexandre, Laradji, Issam H., Roca i Marvà, Francesc Xavier, Gonzàlez, Jordi   +5 more
openaire   +2 more sources