Saltar al contenido principal

ETHNOS_APP

Inicio • Búsqueda • Revistas • Lista 0

Explainable AI for all - A roadmap for inclusive XAI for people with cognitive disabilities

Datos Bibliográficos

ID11237489
AutoresMyrthe L Tielman (0000-0002-7826-5821, Delft University of Technology, autor de correspondencia), Mari Carmen Suárez-Figueroa (0000-0003-3807-5019, Universidad Politécnica de Madrid), Arne Jönsson (0000-0001-9852-5531, Linköping University), Mark A Neerincx (0000-0002-8161-5722, Human Factors (Norway)), Luciano Cavalcante Siebert (0000-0002-7531-3154, Delft University of Technology)
Año2024
Volumen79
Páginas102685
Fecha de publicación2024-12-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaTechnology in Society (JOURNAL)
Identificadores de la revistaISSN: 0160-791X • E-ISSN: 1879-3274
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.techsoc.2024.102685
OpenAlexW4402312599
IdiomaEN
Citas recibidas3
Referencias citadas39

Artificial intelligence (AI) is increasingly prevalent in our daily lives, setting specific requirements for responsible development and deployment: The AI should be explainable and inclusive. Despite substantial research and development investment in explainable AI, there is a lack of effort into making AI explainable and inclusive to people with cognitive disabilities as well. In this paper, we present the first steps towards this research topic. We argue that three main questions guide this research, namely: 1) How explainable should a system be?; 2) What level of understanding can the user reach, and what is the right type of explanation to help them reach this level?; and 3) How can we implement an AI system that can generate the necessary explanations? We present the current state of the art in research on these three topics, the current open questions and the next steps. Finally, we present the challenges specific to bringing these three research topics together, in order to eventually be able to answer the question of how to make AI systems explainable also to people with cognitive disabilities. • Responsible Artificial Intelligence should be both explainable and inclusive. • We present a research roadmap for XAI for people with cognitive disabilities. • Inclusive XAI requires personalisation: so XAI development needs to include users. • Inclusive XAI needs to be adaptive, accountable and responsible

Cognition · Cognitive disabilities · Political science · Psychiatry · Sociology · Artificial Intelligence in Healthcare and Education · Explainable Artificial Intelligence (XAI · Machine Learning in Healthcare · Medicine · Psychology · Gerontology

  • Only practical knowledge or knowing the algorithm? Notions and necessities of explainable artificial intelligence in long-term care

    Open Access•Catharina Margaretha van Leersum, Alexander Peine•AI & Society•2026

  • Responsible scaling of artificial intelligence in healthcare

    Open Access•Dirk R M Lukkien, Henk Herman Nap et al.•Ethics and Information Technology•2025

  • Opacity as a feature, not a flaw

    Open Access•Francisco Herrera, Reyes Calderón•Technology in Society•2026

  • Explanation in artificial intelligence

    Open Access•Tim Miller•Artificial Intelligence•2019

  • Relations of children's motivation for reading to the amount and breadth or their reading.

    Allan Wigfield, John T Guthrie•Journal of Educational Psychology•1997

  • Meaningful Human Control over Autonomous Systems

    Open Access•Filippo Santoni de Sio, Jeroen Van Den Hoven•Frontiers in Robotics and AI•2018

  • XAI—Explainable artificial intelligence

    Open Access•David Gunning, Mark Stefik et al.•Science Robotics•2019

  • A framework for strategic sustainable development

    Open Access•Göran Broman, Karl-Henrik Robèrt et al.•Journal of Cleaner Production•2017

  • A Survey of Methods for Explaining Black Box Models

    Open Access•Riccardo Guidotti, Anna Monreale et al.•ACM Computing Surveys•2019

  • Human-centered XAI

    Open Access•Tjeerd A J Schoonderwoerd, Wiard Jorritsma et al.•International Journal of…•2021

  • Understanding human-robot teams in light of all-human teams

    Open Access•Mustafa Demir, Nathan J McNeese et al.•International Journal of…•2020

  • Psychology and AI at a Crossroads

    Robert R Hoffman, Timothy Miller et al.•The American Journal of Psychology•2022

  • Domesticating AI in medical diagnosis

    Open Access•Robin Williams, Stuart Anderson et al.•Technology in Society•2024

  • Trustworthy AI in the public sector

    Open Access•Alexander Berman, Karl De Fine Licht et al.•Technology in Society•2024

  • Navigating uncertainties of introducing artificial intelligence (AI) in healthcare

    Open Access•Mari S Kannelønning•Technology in Society•2024

  • Digital agency of vulnerable people as experienced by rehabilitation professionals

    Open Access•Piia Silvennoinen, Teemu Rantanen•Technology in Society•2023

  • AI-powered public surveillance systems

    Open Access•Ana Catarina Fontes, Ellen Hohma et al.•Technology in Society•2022

  • Drivers, barriers and social considerations for AI adoption in SCM

    Open Access•Johannes Hangl, Simon Krause et al.•Technology in Society•2023

  • Sustainable AI

    Open Access•Christopher Wilson, Maja Van Der Velden•Technology in Society•2022

  • Responsibility and Control

    Alison Mcintyre, John Martin Fischer et al.•The Philosophical Review•2000

Obras citantes distintas3
Citas por año3
Intervalo de citas2025 - 2026 (2)
Velocidad de citacióncurrent
Altamente citadoNo
Tipos de citaNeutras: 3
Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae