Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloExplainable artificial intelligence enhances the ecological interpretability of black-box species distribution models
Anno di pubblicazione2021
FormatoElettronico
Autore/iRyo, Masahiro; Angelov, Boyan; Mammola, Stefano; Kass, Jamie M.; Benito, Blas M.; Hartig, Florian
Affiliazioni autoriFree Univ Berlin; Berlin Brandenburg Inst Adv Biodivers Res BBIB; Leibniz Ctr Agr Landscape Res ZALF; Assoc Comp Machinery ACM; Natl Res Council CNR; Univ Helsinki; Okinawa Inst Sci & Technol Grad Univ; Univ Alicante; Univ Regensburg
Autori CNR e affiliazioni
  • STEFANO MAMMOLA
Lingua/e
  • inglese
AbstractSpecies distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. During the past two decades, the field has increasingly made use of machine learning approaches for constructing and validating SDMs. Model accuracy has steadily increased as a result, but the interpretability of the fitted models, for example the relative importance of predictor variables or their causal effects on focal species, has not always kept pace. Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions. We describe the rationale behind xAI and provide a list of tools that can be used to help ecological modelers better understand complex model behavior at different scales. As an example, we perform a reproducible SDM analysis in R on the African elephant and showcase some xAI tools such as local interpretable model-agnostic explanation (LIME) to help interpret local-scale behavior of the model. We conclude with what we see as the benefits and caveats of these techniques and advocate for their use to improve the interpretability of machine learning SDMs.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da199
Pagine a205
Pagine totali6
RivistaEcography (Cop.)
Attiva dal 1992
Editore: Blackwell - Oxford
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 0906-7590
Titolo chiave: Ecography (Cop.)
Titolo proprio: Ecography. (Cop.)
Titolo abbreviato: Ecography (Cop.)
Numero volume della rivista44
Fascicolo della rivista2
DOI10.1111/ecog.05360
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000589919100001)
Parole chiaveecological modeling, explainable artificial intelligence, habitat suitability modeling, interpretable machine learning, species distribution model, xAI
Link (URL, URI)-
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Note/Altre informazioni-
Strutture CNR
  • IRSA — Istituto di ricerca sulle acque
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Ryo et al 2020
Tipo documento: application/pdf