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Enhancing trust in automated 3D point cloud data interpretation through explainable counterfactuals

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Our most recent paper introduces a novel framework for augmenting explainability in the interpretation of point cloud data by fusing expert knowledge with counterfactual reasoning. Given the complexity and voluminous nature of point cloud datasets, derived predominantly from LiDAR and 3D scanning technologies, achieving interpretability remains a significant challenge, particularly in smart cities, smart agriculture, and smart forestry. This research posits that integrating expert knowledge with counterfactual explanations – speculative scenarios illustrating how altering input data points could lead to different outcomes – can significantly reduce the opacity of deep learning models processing point cloud data. The proposed optimization-driven framework utilizes expert-informed ad-hoc perturbation techniques to generate meaningful counterfactual scenarios when employing state-of-the-art deep learning architectures. Read the paper here:  https://doi.org/10.1016/j.inffus.2025.103032   and get an overview by listening to this podcast 🙂

 

Yoshua Bengio emphasizes: Deep Learning needs Deep Understanding !

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Yoshua BENGIO from the Canadian Institute for Advanced Research (CIFAR) emphasized during his workshop talk “towards disentangling underlying explanatory factors”  (cool title) at the ICML 2018 in Stockholm, that the key for success in AI/machine learning is to understand the explanatory/causal factors and mechanisms. This means generalizing beyond identical independent data (i.i.d.) – and this is crucial for our domain in medcial AI, because current machine learning theories and models are strongly dependent on this iid assumption, but applications in the real-world (we see this in the medical domain every day!) often require learning and generalizing in areas simply not seen during the training epoch. Humans interestingly are able to protect themselves in such situations, even in situations which they have never seen before. Here a longer talk (1:17:04) at Microsoft Research Redmond on January, 22, 2018 – awesome – enjoy the talk, I recommend it cordially to all of my students!