mlsanityData to 7 Oct 2026

Interpretable deep-learning models to help achieve the Sustainable Development Goals

arXiv 2108.10744 ↗First posted 24 Aug 2021cs.LG cs.AIcited by 18

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Abstract (arXiv)

We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven solutions compliant with the Sustainable Development Goals (SDGs). We highlight the potential of extracting truly-interpretable models from deep-learning methods, for instance via symbolic models obtained through inductive biases, to ensure a sustainable development of AI.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 24 Aug 2021.

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6 of 18 references.

References · 6

  1. 2020Discovering Symbolic Models from Deep Learning with Inductive Biasescited by 335
  2. 2015Inverting Visual Representations with Convolutional Networkscited by 168
  3. 2019Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximationcited by 115
  4. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  5. 2020On Interpretability of Artificial Neural Networks: A Surveycited by 88
  6. 2020A socio-technical framework for digital contact tracingcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 7; 2024: 5; 2025: 0; 2026: 1.

18 papers.

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  1. 2021Enhancing Computational Fluid Dynamics with Machine Learningcited by 137
  2. 2021Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flowscited by 44
  3. 2023Discovering Causal Relations and Equations from Datacited by 33
  4. 2023The transformative potential of machine learning for experiments in fluid mechanicscited by 26
  5. 2022Physics-informed deep-learning applications to experimental fluid mechanicscited by 21
  6. 2022Improving aircraft performance using machine learning: a reviewcited by 19
  7. 2022Predicting the temporal dynamics of turbulent channels through deep learningcited by 10
  8. 2023Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big datacited by 5
  9. 2024Domain adaptation in small-scale and heterogeneous biological datasetscited by 5
  10. 2024Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfercited by 4
  11. 2026Explainable AI: Learning from the Learnerscited by 4
  12. 2023Towards credible visual model interpretation with path attributioncited by 3
6 more
  1. 2024Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Datacited by 3
  2. 2024Decoding complexity: how machine learning is redefining scientific discoverycited by 3
  3. 2023Rethinking interpretation: Input-agnostic saliency mapping of deep visual classifierscited by 2
  4. 2024iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI Systemcited by 1
  5. 2023Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural Indiacited by 0
  6. 2023End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learningcited by 0