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On the Need of an Explainable Artificial Intelligence [PDF]

, 2019
This plenary talk will explore a fascinating nex research field: the Explainable Artificial Intelligence (or XAI), whose goal if the building of explanatory models, to try and overcome the shortcomings of pure statistical learning by providing justifications, understandable by a human, for decisions or predictions made by them.
openaire   +3 more sources

Explainable Artificial Intelligence for Kids [PDF]

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), 2019
Artificial Intelligence (AI) is part of our everyday life and has become one of the most outstanding and strategic technologies of the 21st century. Explainable AI (XAI in short) is expected to endow AI systems with explanation ability when interacting with humans.
openaire   +2 more sources

Instantiating the onEEGwaveLAD Framework for Real-Time Muscle Artefact Identification and Mitigation in EEG Signals

Sensors
While electroencephalography is extremely useful for studying brain activity, EEG data is always contaminated by a wide range of artefacts. Many techniques exist to identify and remove such artefacts, primarily offline, with and without human supervision
Luca Longo, Richard Reilly
doaj   +1 more source

Explainable Artificial Intelligence (XAI) in Insurance

Risks, 2022
Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry ...
Emer Owens, Barry Sheehan, Martin Mullins, Martin Cunneen, Juliane Ressel, German Castignani   +5 more
doaj   +1 more source

Carbon price interval prediction by bidirectional long short-term memory and multi-objective optimization with an asymmetric scaling approach

Energy Reports
Accurate carbon price prediction is essential for decision-making and risk management. Most existing predictive models produce deterministic results and fail to account for uncertainties in carbon prices. To address this limitation, this study introduces
Di Sha, Arne Johannssen, Xianyi Zeng, Zhenglei He, Hanhan Wu, Kim Phuc Tran   +5 more
doaj   +1 more source

A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners

IEEE Access
Due to market deregulation and globalisation, competitive environments in various sectors continuously evolve, leading to increased customer churn. Effectively anticipating and mitigating customer churn is vital for businesses to retain their customer ...
Awais Manzoor, M. Atif Qureshi, Etain Kidney, Luca Longo   +3 more
doaj   +1 more source

Advancing Deliberative Discourse Measurement: The Intersection with Computational Abstract Argumentation in Discourse Quality Evaluations

Systems
This research investigates the potential of computational argumentation, specifically the application of the Abstract Argumentation Framework (AAF), to enhance the evaluation of deliberative quality in public discourse.
Sanjay Kumar, Jane Suiter, Luca Longo
doaj   +1 more source

Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential

FEBS Letters, EarlyView.
Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta, Hari Baskar Balasubramanian, Amir Pasokh, Marietta Zille   +3 more
wiley   +1 more source

Exploring the clinical value of concept-based AI explanations in gastrointestinal disease detection

Scientific Reports
Complex artificial intelligence models, like deep neural networks, have shown exceptional capabilities to detect early-stage polyps and tumors in the gastrointestinal tract.
Andrea M. Storås, Maximilian Dreyer, Frederik Pahde, Sebastian Lapuschkin, Wojciech Samek, Pål Halvorsen, Thomas de Lange, Yuichi Mori, Alexander Hann, Tyler M. Berzin, Sravanthi Parasa, Michael A. Riegler   +11 more
doaj   +1 more source

Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks

IEEE Access
Prediction interval (PI) is a common method to represent predictive uncertainty in regression by deep neural networks. This paper proposes an extension of the prediction interval by using a union of disjoint intervals. Since previous PI methods assumed a
Jaehak Cho, Jae Myung Kim, Seungyub Han, Jungwoo Lee   +3 more
doaj   +1 more source