Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

Pushpendra Rana, Lav R. Varshney

Research output: Contribution to journalReview articlepeer-review

Abstract

Advances in predictive algorithms are revolutionizing how we understand and design effective decision support systems in many sectors. The expanding role of predictive algorithms is part of a broader movement toward using data-driven machine learning (ML) for modalities including images, natural language, speech. This article reviews whether and to what extent predictive algorithms can assist decision-making in forest conservation and management. Although state-of-the-art ML algorithms provide new opportunities, adoption has been slow in forest decision-making. This review shows how domain-specific characteristics, such as system complexity, impose limits on using predictive algorithms in forest conservation and management. We conclude with possible directions for developing new predictive tools and approaches to support meaningful forest decisions through easily interpretable and explainable recommendations.

Original languageEnglish (US)
Article number587178
JournalFrontiers in Forests and Global Change
Volume3
DOIs
StatePublished - Jan 11 2021

Keywords

  • forest system complexity
  • forestry
  • limits to prediction
  • machine learning
  • trustworthy algorithms

ASJC Scopus subject areas

  • Forestry
  • Ecology
  • Global and Planetary Change
  • Nature and Landscape Conservation
  • Environmental Science (miscellaneous)

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