A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional hyper-parametrization (e.g., t-SNE, UMAP, etc.). Recent studies are showing that people often use dimensionality reduction as a black-box regardless of the specific properties the method itself preserves. Hence, evaluating and comparing 2D embeddings is usually qualitatively decided, by setting embeddings side-by-side and letting human judgment decide which embedding is the best. In this work, we propose a quantitative way of evaluating embeddings, that nonetheless places human perception at the center. We run a comparative study, where we ask people to select "good" and "misleading" views between scatterplots of low-dimensional embeddings of image datasets, simulating the way people usually select embeddings. We use the study data as labels for a set of quality metrics for a supervised machine learning model whose purpose is to discover and quantify what exactly people are looking for when deciding between embeddings. With the model as a proxy for human judgments, we use it to rank embeddings on new datasets, explain why they are relevant, and quantify the degree of subjectivity when people select preferred embeddings.

Adrien Bibal, Rémi Cardon, David Alfter, Rodrigo Wilkens, Xiaoou Wang, Thomas François, Patrick Watrin. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Recent technological advances rely on accurate decision support systems that can be perceived as black boxes due to their overwhelming complexity. This lack of transparency can lead to technical, ethical, legal, and trust issues. For example, if the control module of a self-driving car failed at detecting a pedestrian, it becomes crucial to know why the system erred. In some other cases, the decision system may reflect unacceptable biases that can generate distrust. The General Data Protection Regulation (GDPR), approved by the European Parliament in 2018, suggests that individuals should be able to obtain explanations of the decisions made from their data by automated processing, and to challenge those decisions. All these reasons have given rise to the domain of interpretable and explainable AI. AIMLAI aims at gathering researchers, experts and professionals, from inside and outside the domain of AI, interested in the topic of interpretable ML and interpretable AI. The workshop encourages interdisciplinary collaborations, with particular emphasis in knowledge management, Infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters. AIMLAI envisions to become a discussion venue for the advent of novel interpretable algorithms and explainability modules that mediate the communication between complex ML/AI systems and users.

Rémi Cardon, Adrien Bibal, Rodrigo Wilkens, David Alfter, Magali Norré, Adeline Müller, Watrin Patrick, Thomas François. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

$t$-Distributed Stochastic Neighbor Embedding ($t$-SNE) is a well-known dimensionality reduction technique used for the visualization of high-dimensional data. However, despite several improvements,$t$-SNE is not well-suited to handle large datasets. Indeed, for large datasets, the computation time required to obtain the visualizations is still too high to incorporate it in an interactive data exploration process. Since$t$-SNE can be seen as an N -body problem in physics, we present a new variant of$t$-SNE based on a popular algorithm used to solve the N -body problem in physics called Particle-Mesh (PM). The problem is solved by first computing a potential in space and deriving from it the force exerted on each body. As the potential can be computed efficiently using Fast Fourier Transforms (FFTs), this leads to a significant speed up. The mathematical correspondence between$t$-SNE and PM presented in this work could also lead to other future improvements since more advanced PM algorithms have been developed in physics for decades.

Dimensionality reduction is often used for visualization without considering their understanding by users. Multidimensional scaling, for instance, provides an arbitrarily-oriented visualization. However, users can be integrated into the loop to provide clues about their understanding of the visualization. In this paper, we propose an interactive probabilistic multidimensional scaling (iPMDS) approach to compute the visualization with the lowest information loss while taking the information provided by users into account. We show that a more interpretable visualization can be obtained after interacting with the visualization while keeping a good dimensionality reduction accuracy.

Dimensionality reduction (DR) methods are useful when analyzing high dimensional data, in particular, if one wants to visualize them. t-distributed stochastic neighbor embedding (t-SNE), one of the most widely used DR methods, can preserve neighborhood information and reveal groups in embeddings. However, it may not preserve the global structure and fail to reveal the semantic information in the visualization. From a user point-of-view, a DR visualization is useful if it not only reveals hidden structures in the data but also corresponds to the user knowledge. This paper addresses these problems by proposing Hierarchical Constraint t-SNE (HC$t$-SNE), a method that allows users to integrate hierarchical constraints directly into$t$-SNE embeddings. The user constraints are encoded in an explicit tree. We transform the hierarchical information in this tree into a novel regularization term based on triplet constraints among the nodes at different levels in the tree. Our method takes advantage of semantic information provided in class labels and outperforms the original$t$-SNE and two other supervised DR methods in terms of both visual assessment and quality metrics on three classic image datasets: MNIST, Fashion-MNIST and CIFAR10.

For many applications in image analysis, learning models that are invariant to translations and rotations is paramount. This is the case, for example, in medical imaging where the objects of interest can appear at arbitrary positions, with arbitrary orientations. As of today, Convolutional Neural Networks (CNN) are one of the most powerful tools for image analysis. They achieve, thanks to convolutions, an invariance with respect to translations. In this work, we present a new type of convolutional layer that takes advantage of Bessel functions, well known in physics, to build Bessel-CNNs (B-CNNs) that are invariant to all the continuous set of possible rotation angles by design.

The Third Workshop on "Advances in Interpretable Machine Learning and Artificial Intelligence" (AIMLAI) presents contributions in the fields of (i) interpretable ML and AI, i.e., algorithms that are natively interpretable, and (ii) interpretability modules, i.e., explanation layers on top of black-box models, also called post-hoc interpretability. AIMLAI encourages interdisciplinary collaborations with particular emphasis in knowledge management, infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters.