The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Explaining Data-Driven Document Classifications1
Abstract
Many document classification applications require human understanding of the reasons for data-driven classification decisions by managers, client-facing employees, and the technical team. Predictive models treat documents as data to be classified, and document data are characterized by very high dimensionality, often with tens of thousands to millions of variables (words). Unfortunately, due to the high dimensionality, understanding the decisions made by document classifiers is very difficult. This paper begins by extending the most relevant prior theoretical model of explanations for intelligent systems to account for some missing elements. The main theoretical contribution is the definition of a new sort of explanation as a minimal set of words (terms, generally), such that removing all words within this set from the document changes the predicted class from the class of interest. We present an algorithm to find such explanations, as well as a framework to assess such an algorithm’s performance. We demonstrate the value of the new approach with a case study from a real-world document classification task: classifying web pages as containing objectionable content, with the goal of allowing advertisers to choose not to have their ads appear on those pages. A second empirical demonstration on news-story topic classification shows the explanations to be concise and document-specific, and to be capable of providing understanding of the exact reasons for the classification decisions, of the workings of the classification models, and of the business application itself. We also illustrate how explaining the classifications of documents can help to improve data quality and model performance.
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“All in all, counterfactual conditional statements are claimed to enable people to produce utterances that are factually false yet truthful irrespective of the interpretation adopted [32].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [32]
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Self-citation
Metadata
- Authors
- Martens, D., & Provost, F.
- Affiliations
- University of Antwerp (BE)
- New York University (US)
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 2014-03-01
- Volume
- 38
- Issue
- 1
- Pages
- 73-100
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W198744195
- Cited by
- 264
- References
- 68
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 17 · 2023: 49 · 2022: 28 · 2021: 52 · 2020: 29 · 2019: 17 · 2018: 13 · 2017: 12 · 2016: 12 · 2015: 4 · 2014: 6
- FWCI
- 11.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- MIS Quarterly (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- explainable AI, document classification, high-dimensional text data, web page classification, model interpretability, data quality improvement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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