Vaishak Belle

Biographic Data

ID5892524
NAMEVaishak Belle
GIVEN NAMESVaishak
FAMILY NAMEBelle
SIGNATUREBELLE V
AFFILIATIONSUniversity of Edinburgh
ORCID0000-0001-5573-8465
VERIFIEDYes
TOTAL WORKS6
TOTAL CITATIONS0
WORKS AS AUTHOR6
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2019
LATEST PUBLICATION YEAR2025
H-INDEX0

  • Explainability in machine learning

    Open AccessAndreas Bueff, Ioannis Papantonis et al.ARTICLEFrontiers in Education2025

    Introduction Machine learning courses usually focus on getting students prepared to apply various models in real-world settings, but much less attention is given to teaching students the various techniques to explain a model's decision-making process. This gap is particularly concerning given the increasing deployment of AI systems in high-stakes domains where interpretability is crucial for trust, regulatory compliance, and ethical decision-maki…

  • Experiential AI

    Drew Hemment, Dave Murray-Rust et al.ARTICLELeonardo2024References: 15

    Experiential artificial intelligence (AI) is an approach to the design, use, and evaluation of AI in cultural or other real-world settings that foregrounds human experience and context. It combines arts and engineering to support rich and intuitive modes of model interpretation and interaction, making AI tangible and explicit. The ambition is to enable significant cultural works and make AI systems more understandable to nonexperts, thereby stren…

  • Knowledge representation and acquisition for ethical AI

    Open AccessVaishak BelleARTICLEEthics and Information Technology2023

    Machine learning (ML) techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate as recidivism prediction, consumer credit-risk analysis, and insurance pricing. Likewise, in the physical world, ML models are critical components in autonomous agents such as robotic surgeons and self-driving cars. Among the many ethical dimensions that arise in the use of ML technology in such applicatio…

  • Potential Pitfalls With Automatic Sentiment Analysis

    Open AccessEddie L Ungle, Eddie L Ungless et al.ARTICLESocial Science Computer Review2023References: 6

    Automated sentiment analysis can help efficiently detect trends in patients' moods, consumer preferences, political attitudes and more. Unfortunately, like many natural language processing techniques, sentiment analysis can show bias against marginalised groups. We illustrate this point by showing how six popular sentiment analysis tools respond to sentences about queer identities, expanding on existing work on gender, ethnicity and disability. W…

  • Toward Fairness, Morality and Transparency in Artificial Intelligence through Experiential AI

    Drew Hemment, Vaishak Belle et al.ARTICLELeonardo2019

    October 01 2019 Toward Fairness, Morality and Transparency in Artificial Intelligence through Experiential AI Drew Hemment, Drew Hemment University of Edinburgh, Email: [email protected] Search for other works by this author on: This Site Google Scholar Vaishak Belle, Vaishak Belle University of Edinburgh Search for other works by this author on: This Site Google Scholar Ruth Aylett, Ruth Aylett Heriot-Watt University Search for other works b…

  • Toward Fairness, Morality and Transparency in Artificial Intelligence through Experiential AI

    Drew Hemment, Vaishak Belle et al.ARTICLELeonardo2019References: 1

    October 01 2019 Toward Fairness, Morality and Transparency in Artificial Intelligence through Experiential AI Drew Hemment, Drew Hemment University of Edinburgh, Email: [email protected] Search for other works by this author on: This Site Google Scholar Vaishak Belle, Vaishak Belle University of Edinburgh Search for other works by this author on: This Site Google Scholar Ruth Aylett, Ruth Aylett Heriot-Watt University Search for other works b…