the open
    research
    Contribute
    Back to the reading lists

    Safety, alignment & interpretability· essay· 2018

    Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

    Cynthia Rudin (first author) · 2018 · published 2019

    Read the original

    DOI 10.1038/s42256-019-0048-xarXiv 1811.10154OpenAlex W2945976633

    On the reading lists

    Why it matters

    Challenged post-hoc explanation practices and argued for transparent models in high-stakes decisions.

    Condensed from the expert lists' notes.

    The record

    First released
    2018 (from arXiv 1811.10154)
    Published
    2019
    Citations
    11,148 citations, as counted by Semantic Scholar logoSemantic Scholar (non-commercial licence)
    Named by
    both expert lists

    Editor's notes

    • Classed as essay: A Nature Machine Intelligence Perspective that argues a position rather than reporting a new method. (AI draft (Claude), not yet reviewed, 2026-10-29)

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and Semantic Scholar logoSemantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.