Counterfactual explanations (CFEs) highlight what changes to a model's input would have changed its prediction in a particular way. CFEs have gained considerable traction as a psychologically grounded solution for explainable artificial intelligence (XAI). Recent innovations introduce the notion of computational plausibility for automatically generated CFEs, enhancing their robustness by exclusively creating plausible explanations. However, practical benefits of such a constraint on user experience and behavior is yet unclear. In this study, we evaluate objective and subjective usability of computationally plausible CFEs in an iterative learning design targeting novice users. We rely on a novel, game-like experimental design, revolving around an abstract scenario. Our results show that novice users actually benefit less from receiving computationally plausible rather than closest CFEs that produce minimal changes leading to the desired outcome. Responses in a post-game survey reveal no differences in terms of subjective user experience between both groups. Following the view of psychological plausibility as comparative similarity, this may be explained by the fact that users in the closest condition experience their CFEs as more psychologically plausible than the computationally plausible counterpart. In sum, our work highlights a little-considered divergence of definitions of computational plausibility and psychological plausibility, critically confirming the need to incorporate human behavior, preferences and mental models already at the design stages of XAI approaches. In the interest of reproducible research, all source code, acquired user data, and evaluation scripts of the current study are available: https://github.com/ukuhl/PlausibleAlienZoo

Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, \textit{Spherical-deconvolution Informed Filtering of Tractograms} (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable to judge the plausibility of individual streamlines since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of plausible and implausible streamlines with accuracy above 80%. The software code used in the paper and pretrained weights of the classifier are distributed freely via the Github repository https://github.com/djoerch/randomised_filtering.

Recent trends in computing hardware have placed increasing emphasis on neuromorphic architectures implementing machine learning (ML) algorithms directly in hardware. Such bio-inspired approaches, through in-memory computation and massive parallelism, excel in new classes of computational problems and offer promising advantages with respect to power consumption error resiliency. While CMOS-based neuromorphic computing (NC) implementations have made substantial progress recently, new materials and physical mechanisms may ultimately provide better opportunities for energy efficiency and scaling [1, 2, 3]. A specific functionality required in NC applications is the ability to mimic synaptic connections and plasticity by allowing the storage of large numbers of interconnected and continuously adaptable resistance values. Several candidate memory technologies such as MRAM, ReRAM, PCM, CeRAM, are emerging to cover this behavior using different physical mechanisms [4, 5, 6, 7]. Among these, ReRAM is attractive for its simplicity of materials and device structure, providing the necessary CMOS compatibility and scalability [8]. ReRAM is essentially a two terminal nanoscale electrochemical cell, whose variable resistance state is based on manipulation of the point defect configuration in the oxide material (depicted in Figure 1).

This post contains a list of the AI-related seminars that are scheduled to take place between 9 May 2022 and 30 June 2022. All events detailed here are free and open for anyone to attend virtually. Note: this event runs for four days – 9-12 May. Instance-adaptive data compression: Improving Neural Codecs by Training on the Test Set Speaker: Ties van Rozendaal Organised by: University of California, Irvine The live stream is here. Kernel-based robust inference for intractable likelihood models Speaker: François-Xavier Briol Organised by: Finnish Centre for AI Zoom link is here.

In this work, we present a naive initialization scheme for word vectors based on a dense, independent co-occurrence model and provide preliminary results that suggest it is competitive and warrants further investigation. Specifically, we demonstrate through information-theoretic minimum description length (MDL) probing that our model, EigenNoise, can approach the performance of empirically trained GloVe despite the lack of any pre-training data (in the case of EigenNoise). We present these preliminary results with interest to set the stage for further investigations into how this competitive initialization works without pre-training data, as well as to invite the exploration of more intelligent initialization schemes informed by the theory of harmonic linguistic structure. Our application of this theory likewise contributes a novel (and effective) interpretation of recent discoveries which have elucidated the underlying distributional information that linguistic representations capture from data and contrast distributions.

Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This review paper investigates a wide range of model-free and model-based algorithms for viewpoint and path planning for 3D reconstruction of large-scale objects. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3D reconstruction purposes. In addition to discussing the evaluation strategies, this paper also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.

Recently, Radiant Earth Foundation released a land cover dataset for South America, continuing the work they had been doing in other parts of the world and in connection with other areas of interest. In connection with previous posts, this article explains how to train a segmentation model based on this dataset in just 4 steps. Specifically, we will explain in detail how to train a model for classifying the use of cropland, based on the mentioned dataset. The released dataset comprises labels and satellite imagery from Sentinel-1, Sentinel-2 and Landsat 8 missions for classifying the uses of South American land (if you would like to learn more about satellite imagery sources, click here). Each pixel is identified as one of the possible seven land classes: water, natural bare ground, artificial bare ground, woody vegetation, cultivated ground, semi-cultivated ground, and permanent snow/ice.

To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field of explainable artificial intelligence (XAI) has resurfaced as a topic of active research, offering approaches to address the "how" and "why" of automated decision-making. Within this domain, counterfactual explanations (CFEs) have gained considerable traction as a psychologically grounded approach to generate post-hoc explanations. To do so, CFEs highlight what changes to a model's input would have changed its prediction in a particular way. However, despite the introduction of numerous CFE approaches, their usability has yet to be thoroughly validated at the human level. Thus, to advance the field of XAI, we introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. The Alien Zoo provides the means to evaluate usability of CFEs for gaining new knowledge from an automated system, targeting novice users in a domain-general context. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study. Our results suggest that users benefit from receiving CFEs compared to no explanation, both in terms of objective performance in the proposed iterative learning task, and subjective usability. With this work, we aim to equip research groups and practitioners with the means to easily run controlled and well-powered user studies to complement their otherwise often more technology-oriented work. Thus, in the interest of reproducible research, we provide the entire code, together with the underlying models and user data.

We study the problem of $(\epsilon,\delta)$-differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is when the loss is smooth and non-negative but not necessarily Lipschitz (such as the squared loss). For this case, we establish an upper bound on the excess population risk of $\tilde{O}\left(\frac{\Vert w^*\Vert}{\sqrt{n}} + \min\left\{\frac{\Vert w^* \Vert^2}{(n\epsilon)^{2/3}},\frac{\sqrt{d}\Vert w^*\Vert^2}{n\epsilon}\right\}\right)$, where $n$ is the number of samples, $d$ is the dimension of the problem, and $w^*$ is the minimizer of the population risk. Apart from the dependence on $\Vert w^\ast\Vert$, our bound is essentially tight in all parameters. In particular, we show a lower bound of $\tilde{\Omega}\left(\frac{1}{\sqrt{n}} + {\min\left\{\frac{\Vert w^*\Vert^{4/3}}{(n\epsilon)^{2/3}}, \frac{\sqrt{d}\Vert w^*\Vert}{n\epsilon}\right\}}\right)$. We also revisit the previously studied case of Lipschitz losses [SSTT20]. For this case, we close the gap in the existing work and show that the optimal rate is (up to log factors) $\Theta\left(\frac{\Vert w^*\Vert}{\sqrt{n}} + \min\left\{\frac{\Vert w^*\Vert}{\sqrt{n\epsilon}},\frac{\sqrt{\text{rank}}\Vert w^*\Vert}{n\epsilon}\right\}\right)$, where $\text{rank}$ is the rank of the design matrix. This improves over existing work in the high privacy regime. Finally, our algorithms involve a private model selection approach that we develop to enable attaining the stated rates without a-priori knowledge of $\Vert w^*\Vert$.

This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divides in order to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness for producing just outcomes for society's most marginalized. The article is organized according to nine major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench "bias," are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI's long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.