From local explanations to global understanding with explainable AI for trees

Scott Lundberg; Gabriel Erion; Hugh Chen; Alex J. DeGrave; Jordan M. Prutkin; Bala G. Nair; Ronit Katz; Jonathan Himmelfarb; Nisha Bansal; Su‐In Lee

doi:https://doi.org/10.1038/s42256-019-0138-9

From local explanations to global understanding with explainable AI for trees

2020 · Nature Machine Intelligence · 9,936 citations · 2 from inside this corpus

Scott Lundberg, Gabriel Erion, Hugh Chen, Alex J. DeGrave, Jordan M. Prutkin, Bala G. Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su‐In Lee

No abstract in the source record.

7 of 7 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

this paper works it cites works citing it node size = global citations · hover for the full title

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw…→From local explanations to global understanding with explainable AI for treesLegacy edge — source detail unavailable
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications→From local explanations to global understanding with explainable AI for treesLegacy edge — source detail unavailable
From local explanations to global understanding with explainable AI for trees→XGBoostLegacy edge — source detail unavailable
From local explanations to global understanding with explainable AI for trees→"Why Should I Trust You?"Legacy edge — source detail unavailable
From local explanations to global understanding with explainable AI for trees→On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Deg…Legacy edge — source detail unavailable
From local explanations to global understanding with explainable AI for trees→Explaining prediction models and individual predictions with feature contributionsLegacy edge — source detail unavailable
From local explanations to global understanding with explainable AI for trees→Anchors: High-Precision Model-Agnostic ExplanationsLegacy edge — source detail unavailable
Anchors: High-Precision Model-Agnostic Explanations→"Why Should I Trust You?"Legacy edge — source detail unavailable
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Deg…→Explaining prediction models and individual predictions with feature contributionsLegacy edge — source detail unavailable
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications→"Why Should I Trust You?"Legacy edge — source detail unavailable
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications→Anchors: High-Precision Model-Agnostic ExplanationsLegacy edge — source detail unavailable
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications→On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Deg…Legacy edge — source detail unavailable
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw…→"Why Should I Trust You?"Legacy edge — source detail unavailable
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw…→Anchors: High-Precision Model-Agnostic ExplanationsLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
XGBoost	2016	52,304
"Why Should I Trust You?"	2016	16,210
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Deg…	2024	13,528
Explaining prediction models and individual predictions with feature contributions	2013	2,302
Anchors: High-Precision Model-Agnostic Explanations	2018	2,124

What cites it, inside the corpus

Paper	Year	Cited
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw…	2023	1,679
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications	2021	1,424

Links

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Topics

Explainable Artificial Intelligence (XAI)	Computer Science
Bayesian Modeling and Causal Inference	Computer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

supports10 author record(s) attached.
supports39 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:40+00:00.

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