Exploring Interpretability in Open Government Data with ChatGPT

Conference Paper (2024)
Author(s)

Raissa Barcellos (Universidade do Estado do Rio de Janeiro)

Flavia Bernardini (Universidade Federal Fluminense)

José Viterbo (Universidade Federal Fluminense)

A.M.G. Zuiderwijk-van Eijk (TU Delft - Information and Communication Technology)

Research Group
Information and Communication Technology
DOI related publication
https://doi.org/10.1145/3657054.3657079
More Info
expand_more
Publication Year
2024
Language
English
Research Group
Information and Communication Technology
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
186-195
ISBN (electronic)
9798400709883
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The global initiative supporting open government data (OGD) has witnessed significant strides in the last decade. This study delves into the prospective integration of Artificial Intelligence (AI) with Hippolyta, a framework meticulously crafted to amplify the interpretability of government data. The aim is to scrutinize the viability of this integration, conducting a technical investigation in the realms of open government data and artificial intelligence. In contributing to the expansive field of OGD, this research focuses on elucidating the interpretability of data originating from governmental sources. Through an exploration of the technical feasibility surrounding the fusion of AI with Hippolyta, we aim to pave the path for advancements, fostering heightened interpretability and overarching enhancements in the understanding of government data.

Files

3657054.3657079.pdf
(pdf | 0.927 Mb)
- Embargo expired in 11-12-2024
License info not available