Evaluating Human-Centered AI Explanations: Introduction of an XAI Evaluation Framework for Fact-Checking Article Swipe
Vera Schmitt
,
Balázs Patrik Csomor
,
Joachim Meyer
,
Luis-Felipe Villa-Areas
,
Charlott Jakob
,
Tim Polzehl
,
Sebastian Möller
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3643491.3660283
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3643491.3660283
The rapidly increasing amount of online information and the advent of Generative Artificial Intelligence (GenAI) make the manual verification of information impractical. Consequently, AI systems are deployed to detect disinformation and deepfakes. Prior studies have indicated that combining AI and human capabilities yields enhanced performance in detecting disinformation. Furthermore, the European Union (EU) AI Act mandates human supervision for AI applications in areas impacting essential human rights, like freedom of speech, necessitating that AI systems be transparent and provide adequate explanations to ensure comprehensibility. Extensive research has been conducted on incorporating explainability (XAI) attributes to augment AI transparency, yet these often miss a human-centric assessment. The effectiveness of such explanations also varies with the user's prior knowledge and personal attributes. Therefore, we developed a framework for validating XAI features for the collaborative human-AI fact-checking task. The framework allows the testing of XAI features with objective and subjective evaluation dimensions and follows human-centric design principles when displaying information about the AI system to the users. The framework was tested in a crowdsourcing experiment with 433 participants, including 406 crowdworkers and 27 journalists for the collaborative disinformation detection task. The tested XAI features increase the AI system's perceived usefulness, understandability, and trust. With this publication, the XAI evaluation framework is made open source.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3643491.3660283
- OA Status
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All OpenAlex metadata
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https://openalex.org/W4399261643Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3643491.3660283Digital Object Identifier
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Evaluating Human-Centered AI Explanations: Introduction of an XAI Evaluation Framework for Fact-CheckingWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-06-01Full publication date if available
- Authors
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Vera Schmitt, Balázs Patrik Csomor, Joachim Meyer, Luis-Felipe Villa-Areas, Charlott Jakob, Tim Polzehl, Sebastian MöllerList of authors in order
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https://doi.org/10.1145/3643491.3660283Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1145/3643491.3660283Direct OA link when available
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Computer science, Data science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 2, 2024: 4Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.systems | 24, 74 |
| abstract_inverted_index.testing | 139 |
| abstract_inverted_index.European | 50 |
| abstract_inverted_index.adequate | 79 |
| abstract_inverted_index.deployed | 26 |
| abstract_inverted_index.enhanced | 43 |
| abstract_inverted_index.features | 128, 142, 190 |
| abstract_inverted_index.human-AI | 132 |
| abstract_inverted_index.increase | 191 |
| abstract_inverted_index.mandates | 55 |
| abstract_inverted_index.personal | 118 |
| abstract_inverted_index.research | 85 |
| abstract_inverted_index.system's | 194 |
| abstract_inverted_index.Extensive | 84 |
| abstract_inverted_index.combining | 37 |
| abstract_inverted_index.conducted | 88 |
| abstract_inverted_index.detecting | 46 |
| abstract_inverted_index.detection | 185 |
| abstract_inverted_index.developed | 122 |
| abstract_inverted_index.essential | 64 |
| abstract_inverted_index.framework | 124, 136, 165, 206 |
| abstract_inverted_index.impacting | 63 |
| abstract_inverted_index.including | 175 |
| abstract_inverted_index.indicated | 35 |
| abstract_inverted_index.knowledge | 116 |
| abstract_inverted_index.objective | 144 |
| abstract_inverted_index.perceived | 195 |
| abstract_inverted_index.Artificial | 12 |
| abstract_inverted_index.Generative | 11 |
| abstract_inverted_index.Therefore, | 120 |
| abstract_inverted_index.attributes | 93 |
| abstract_inverted_index.deepfakes. | 31 |
| abstract_inverted_index.dimensions | 148 |
| abstract_inverted_index.displaying | 155 |
| abstract_inverted_index.evaluation | 147, 205 |
| abstract_inverted_index.experiment | 171 |
| abstract_inverted_index.increasing | 2 |
| abstract_inverted_index.principles | 153 |
| abstract_inverted_index.subjective | 146 |
| abstract_inverted_index.validating | 126 |
| abstract_inverted_index.assessment. | 104 |
| abstract_inverted_index.attributes. | 119 |
| abstract_inverted_index.information | 6, 20, 156 |
| abstract_inverted_index.journalists | 180 |
| abstract_inverted_index.performance | 44 |
| abstract_inverted_index.supervision | 57 |
| abstract_inverted_index.transparent | 76 |
| abstract_inverted_index.usefulness, | 196 |
| abstract_inverted_index.Furthermore, | 48 |
| abstract_inverted_index.Intelligence | 13 |
| abstract_inverted_index.applications | 60 |
| abstract_inverted_index.capabilities | 41 |
| abstract_inverted_index.crowdworkers | 177 |
| abstract_inverted_index.explanations | 80, 109 |
| abstract_inverted_index.impractical. | 21 |
| abstract_inverted_index.publication, | 202 |
| abstract_inverted_index.verification | 18 |
| abstract_inverted_index.Consequently, | 22 |
| abstract_inverted_index.collaborative | 131, 183 |
| abstract_inverted_index.crowdsourcing | 170 |
| abstract_inverted_index.effectiveness | 106 |
| abstract_inverted_index.fact-checking | 133 |
| abstract_inverted_index.human-centric | 103, 151 |
| abstract_inverted_index.incorporating | 90 |
| abstract_inverted_index.necessitating | 71 |
| abstract_inverted_index.participants, | 174 |
| abstract_inverted_index.transparency, | 97 |
| abstract_inverted_index.disinformation | 29, 184 |
| abstract_inverted_index.explainability | 91 |
| abstract_inverted_index.disinformation. | 47 |
| abstract_inverted_index.comprehensibility. | 83 |
| abstract_inverted_index.understandability, | 197 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.91501848 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |