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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Replication Package - From Research to Practice: A Survey of XAI Process Frameworks

Replication Package - From Research to Practice: A Survey of XAI Process Frameworks

Abstract

{"references": ["Garrick Cabour, Andr\u00e9s Morales, \u00c9lise Ledoux, and Samuel Bassetto. 2021. Towards an Explanation Space to Align Humans and Explainable-AI Teamwork. https://doi.org/10.48550/arXiv.2106.01503 arXiv:2106.01503 [cs].", "Haomin Chen, Catalina Gomez, Chien-Ming Huang, and Mathias Unberath. 2022. Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review. https://doi.org/10.48550/arXiv.2112.12596 arXiv:2112.12596 [cs, eess].", "Douglas Cirqueira, Dietmar Nedbal, Markus Helfert, and Marija Bezbradica. 2020. Scenario-Based Requirements Elicitation for User-Centric Explainable AI. In Machine Learning and Knowledge Extraction, Andreas Holzinger, Peter Kieseberg, A Min Tjoa, and Edgar Weippl (Eds.). Springer International Publishing, Cham, 321\u2013341.", "Upol Ehsan, Q. Vera Liao, Samir Passi, Mark O. Riedl, and Hal Daume III. 2022. Seamful XAI: Operationalizing Seamful Design in Explainable AI. https://doi.org/10.48550/arXiv.2211.06753 arXiv:2211.06753 [cs].", "Malin Eiband, Hanna Schneider, Mark Bilandzic, Julian Fazekas-Con, Mareike Haug, and Heinrich Hussmann. 2018. Bringing Transparency Design into Practice. In 23rd International Conference on Intelligent User Interfaces (IUI'18). Association for Computing Machinery, New York, NY, USA, 211\u2013223. https://doi.org/10.1145/3172944.3172961", "Umm-e Habiba, Justus Bogner, and Stefan Wagner. 2022. Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements. https://doi.org/10.48550/arXiv.2206. 01507 arXiv:2206.01507 [cs]", "Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, and Ghassan Hamarneh. 2022. EUCA: the End-User-Centered Explainable AI Framework. https://doi.org/10.48550/arXiv.2102.02437 arXiv:2102.02437 [cs]", "Q. Vera Liao, Milena Pribi\u0107, Jaesik Han, Sarah Miller, and Daby Sow. 2021. Question-Driven Design Process for Explainable AI User Experiences. https://doi.org/10.48550/arXiv.2104.03483 arXiv:2104.03483 [cs]", "Sina Mohseni, Niloofar Zarei, and Eric D. Ragan. 2020. A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems. https://doi.org/10.48550/arXiv.1811.11839 arXiv:1811.11839 [cs]", "Gesina Schwalbe and Bettina Finzel. 2023. A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts. Data Mining and Knowledge Discovery (Jan. 2023). https://doi.org/10.1007/s10618-022-00867-8 arXiv:2105.07190 [cs]"]}

Replication package for the ICSE NIER Submission "From Research to Practice: A Survey of XAI Process Frameworks." Provides details about the methods and data used in our analysis. For more information, please take a look at the README.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
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    visibility views 12
    download downloads 14
  • 12
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visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
12
14