• tag:aim-pro.eu,2026:oer/ac97f0396dda
• https://arxiv.org/abs/1811.10154
• arxiv:1811.10154

This engine's id, the source URI, and any persistent identifier the source published.

• Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

The title of this resource, not of the record or repository it came from.

• dcmitype:Text
• preprint

A DCMI Type term for the form held, plus the source's own genre label as declared.

• Cynthia Rudin

Only agents the source named as creators. A repository owner is not made an author by default.

• Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to \textit{explain} black box models, rather than creating models that are \textit{interpretable} in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward -- it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.

The abstract, and the depositor's additional notes after it when the source has a field for them.

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• cs.LG (arxiv)

Declared keywords and taxonomy terms, each carrying its scheme.

• en

The language of the content as the language gate read it.

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The licence document, as a URI where the gate resolved one.

• CC-BY-SA-4.0 — assessed as SHARE_ALIKE by the harvester's licence gate. Conditions: attribution required, adaptations must carry the same licence. open but ShareAlike/copyleft — derivatives keep the license

The verdict and the conditions, in words.

• open

The access condition the source declared. Not the licence.

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One value per form held — the original and the extracted text.

• 84815 bytes (extracted text)

Sizes in bytes, each saying which form it measures.

• 2018-11-26

When the resource was published. Not when it was collected.

• 2019-09-22

When the source's own record was last changed.

• Nature Machine Intelligence, Vol 1, May 2019, 206-215

Where the work was published, in the source's own words: a journal with its volume and pages, a conference, an imprint.

• doi:10.1093/biostatistics/kxy002

Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.

• Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
• Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it.

Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.

• URI: tag:aim-pro.eu,2026:oer/ac97f0396dda
• URI: https://arxiv.org/abs/1811.10154
• ARXIV: 1811.10154

This engine's identifier for the resource, the source URI, and any persistent identifier the source published.

• Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

• en

The language of the content, as the language gate read it off the converted text. The language the source declared is kept separately.

• Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to \textit{explain} black box models, rather than creating models that are \textit{interpretable} in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward -- it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.

The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.

• stat.ML
• cs.LG

• v3

• revised

Draft, final, revised or unavailable, and only when the source says so. A first version is not thereby final.

• Cynthia Rudin — author

Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.

• URI: tag:aim-pro.eu,2026:oer/ac97f0396dda/record

The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.

• AIM-PRO WP3 OER harvester (arXiv) — creator

Who generated this record and when — a statement about the record, not about the resource.

• LOMv1.0
• aimpro-oer-profile/1

LOMv1.0, and the profile this was built against.

• en

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One value per form held: the original as the source published it, and the Markdown this engine extracted.

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Bytes. The original's, because the resource is the file and not our conversion of it.

• https://arxiv.org/abs/1811.10154

Where the source serves it.

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Only from a declaration that maps onto LOM's vocabulary. A file format is not a declaration.

• no — free to use

No: the licence gate refuses anything it cannot establish open reuse terms for, before the bytes are requested.

• yes — attribution required, share-alike (CC-BY-SA-4.0)

Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.

• CC-BY-SA-4.0 · http://creativecommons.org/licenses/by-sa/4.0/. Conditions: adaptation permitted, adaptations must carry the same licence, attribution required.

The SPDX id, the licence URI and the conditions. LOM has no element for any of the three, so this is where they survive.

• ispartof

• Nature Machine Intelligence, Vol 1, May 2019, 206-215

• references: doi:10.1093/biostatistics/kxy002

The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.