Počet záznamů: 1  

Well-being impact assessment of artificial intelligence-A search for causality and proposal for an open platform for well-being impact assessment of AI systems

  1. 1.
    SYSNO ASEP0571239
    Druh ASEPJ - Článek v odborném periodiku
    Zařazení RIVJ - Článek v odborném periodiku
    Poddruh JČlánek ve WOS
    NázevWell-being impact assessment of artificial intelligence-A search for causality and proposal for an open platform for well-being impact assessment of AI systems
    Tvůrce(i) Havrda, M. (CZ)
    Klocek, Adam (PSU-E) ORCID, RID, SAI
    Číslo článku102294
    Zdroj.dok.Evaluation and Program Planning. - : Elsevier - ISSN 0149-7189
    Roč. 99, srpen (2023)
    Poč.str.43 s.
    Forma vydáníTištěná - P
    Jazyk dok.eng - angličtina
    Země vyd.GB - Velká Británie
    Klíč. slovaartificial intelligence ; well-being ; impact assessment ; causality ; open science ; complexity
    Vědní obor RIVAN - Psychologie
    Obor OECDPsychology (including human - machine relations)
    Způsob publikováníOmezený přístup
    Institucionální podporaPSU-E - RVO:68081740
    UT WOS001001561000001
    EID SCOPUS85159587686
    DOI10.1016/j.evalprogplan.2023.102294
    AnotaceIn recent years, the well-being impact assessment approach has been applied in the area of Artificial Intelligence (AI). Existing well-being frameworks and tools provide a relevant starting point. Taking into account its multidimensional nature, well-being assessment is well suited to assess both the expected positive effects of the technology as well as unintended negative consequences. To-date the establishment of causal links mostly stems from intuitive causal models. Such approaches neglect the fact that to prove causal links between the operation of an AI system and observed effects is difficult due to the immense complexity of the socio-technical context. This article aims at providing a framework for ascertaining the attribution of effects of observed impact of AI on well-being. An elaborated approach to impact assessment potentially enabling causal inferences is demonstrated. Furthermore, a new Open Platform for Well-Being Impact Assessment of AI systems (OPIA) is introduced, which is based on a distributed community to build reproducible evidence through effective identification, refinement, iterative testing, and cross-validation of expected causal structures.
    PracovištěPsychologický ústav
    KontaktŠtěpánka Halamová, Halamova@praha.psu.cas.cz, Tel.: 222 222 096
    Rok sběru2024
    Elektronická adresahttps://www.sciencedirect.com/science/article/pii/S014971892300071X?via%3Dihub
Počet záznamů: 1  

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