When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates

Research article (Expert Systems with Applications, 2022) · cited 26× · AI/ML
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When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates

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When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates is a scholarly article[1].

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  • When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates's instance of is recorded as scholarly article[2].

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APA 4ort.xyz Knowledge Graph. (2026). When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates. Retrieved May 24, 2026, from https://4ort.xyz/entity/when-stakes-are-high-balancing-accuracy-and-transparency-with-model-agnostic-interpretable-data-driven-surrogates
MLA “When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/when-stakes-are-high-balancing-accuracy-and-transparency-with-model-agnostic-interpretable-data-driven-surrogates.
BibTeX @misc{4ortxyz_when-stakes-are-high-balancing-accuracy-and-transparency-with-model-agnostic-interpretable-data-driven-surrogates_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates}}, year = {2026}, url = {https://4ort.xyz/entity/when-stakes-are-high-balancing-accuracy-and-transparency-with-model-agnostic-interpretable-data-driven-surrogates}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates — https://4ort.xyz/entity/when-stakes-are-high-balancing-accuracy-and-transparency-with-model-agnostic-interpretable-data-driven-surrogates (retrieved 2026-05-24)

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