From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space

Deep Neural Networks are prone to learning spurious correlations embedded in the training data, leading to potentially biased predictions. This poses risks when deploying these models for high-stake decision-making, such as in medical applications. Current methods for post-hoc model correction either require input-level annotations which are only possible for spatially localized biases, or augment the latent feature space, thereby hoping to enforce the right reasons. We present a novel method for model correction on the concept level that explicitly reduces model sensitivity towards biases via gradient penalization. When modeling biases via Concept Activation Vectors, we highlight the importance of choosing robust directions, as traditional regression-based approaches such as Support Vector Machines tend to result in diverging directions. We effectively mitigate biases in controlled and real-world settings on the ISIC, Bone Age, ImageNet and CelebA datasets using VGG, ResNet and EfficientNet architectures. Code is available on https://github.com/frederikpahde/rrclarc.Comment: 35 pages (9 pages manuscript, 2 pages references, 24 pages appendix

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence, approaches are available to explore the reasoning behind those complex models' predictions. One class of approaches are post-hoc attribution methods, among which Layer-wise Relevance Propagation (LRP) shows high performance. However, the attempt at understanding a DNN's reasoning often stops at the attributions obtained for individual samples in input space, leaving the potential for deeper quantitative analyses untouched. As a manual analysis without the right tools is often unnecessarily labor intensive, we introduce three software packages targeted at scientists to explore model reasoning using attribution approaches and beyond: (1) Zennit - a highly customizable and intuitive attribution framework implementing LRP and related approaches in PyTorch, (2) CoRelAy - a framework to easily and quickly construct quantitative analysis pipelines for dataset-wide analyses of explanations, and (3) ViRelAy - a web-application to interactively explore data, attributions, and analysis results.Comment: 10 pages, 3 figure

Laser-induced breakdown spectroscopy: a tool for real-time, in vitro and in vivo identification of carious teeth

BACKGROUND: Laser Induced Breakdown Spectroscopy (LIBS) can be used to measure trace element concentrations in solids, liquids and gases, with spatial resolution and absolute quantifaction being feasible, down to parts-per-million concentration levels. Some applications of LIBS do not necessarily require exact, quantitative measurements. These include applications in dentistry, which are of a more "identify-and-sort" nature – e.g. identification of teeth affected by caries. METHODS: A one-fibre light delivery / collection assembly for LIBS analysis was used, which in principle lends itself for routine in vitro / in vivo applications in a dental practice. A number of evaluation algorithms for LIBS data can be used to assess the similarity of a spectrum, measured at specific sample locations, with a training set of reference spectra. Here, the description has been restricted to one pattern recognition algorithm, namely the so-called Mahalanobis Distance method. RESULTS: The plasma created when the laser pulse ablates the sample (in vitro / in vivo), was spectrally analysed. We demonstrated that, using the Mahalanobis Distance pattern recognition algorithm, we could unambiguously determine the identity of an "unknown" tooth sample in real time. Based on single spectra obtained from the sample, the transition from caries-affected to healthy tooth material could be distinguished, with high spatial resolution. CONCLUSIONS: The combination of LIBS and pattern recognition algorithms provides a potentially useful tool for dentists for fast material identification problems, such as for example the precise control of the laser drilling / cleaning process

Does observability affect prosociality?

The observation of behaviour is a key theoretical parameter underlying a number of models of prosociality. However, the empirical findings showing the effect of observability on prosociality are mixed. In this meta-analysis, we explore the boundary conditions that may account for this variability, by exploring key theoretical and methodological moderators of this link. We identified 117 papers yielding 134 study level effects (Total N = 788, 164) and found a small but statistically significant, positive association between observability and prosociality (r = .141, 95% CI = .106, .175). Moderator analysis showed that observability produced stronger effects on prosociality (1) in the presence of passive observers (i.e., people whose role was to only observe participants) vs perceptions of being watched, (2) when participants decisions were consequential (vs non-consequential), (3) when the studies were performed in the laboratory (as opposed to in the field/online), (4) when studies used repeated measures (instead of single games) and (5) when studies involved social dilemmas (instead of bargaining games). These effects show the conditions under which observability effects on prosociality will be maximally observed. We describe the theoretical and practical significance of 14 these results

Codevelopment Between Key Personality Traits and Alcohol Use Disorder From Adolescence Through Young Adulthood

ObjectivePersonality traits related to negative emotionality and low constraint are strong correlates of alcohol use disorder (AUD), but few studies have evaluated the prospective interplay between these traits and AUD symptoms from adolescence to young adulthood.MethodThe Minnesota Twin Family Study (N = 2,769) was used to examine the developmental interplay between AUD symptoms and three personality measures of constraint, negative emotionality, and aggressive undercontrol from ages 17 to 29.ResultsResults from random‐intercept, cross‐lagged panel models showed that low constraint and aggressive undercontrol predicted subsequent rank‐order increases in AUD symptoms from ages 17 to 24. AUD symptoms did not predict rank‐order change in these traits from ages 17 to 24. There was support for both cross‐effects from ages 24 to 29. Biometric analysis of the twin data showed genetic influences accounted for most of the phenotypic correlations over time.ConclusionResults are consistent with the notion that personality traits related to low constraint and aggressive undercontrol are important vulnerability/predisposition factors for the development of early adult AUD. In later young adulthood, there is more evidence for the simultaneous codevelopment of personality and AUD. Implications are addressed with attention to personality‐based risk assessments and targeted AUD prevention approaches.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142935/1/jopy12311.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142935/2/jopy12311_am.pd

Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence

With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable concepts in the latent space. Commonly, CAVs are computed by leveraging linear classifiers optimizing the separability of latent representations of samples with and without a given concept. However, in this paper we show that such a separability-oriented computation leads to solutions, which may diverge from the actual goal of precisely modeling the concept direction. This discrepancy can be attributed to the significant influence of distractor directions, i.e., signals unrelated to the concept, which are picked up by filters (i.e., weights) of linear models to optimize class-separability. To address this, we introduce pattern-based CAVs, solely focussing on concept signals, thereby providing more accurate concept directions. We evaluate various CAV methods in terms of their alignment with the true concept direction and their impact on CAV applications, including concept sensitivity testing and model correction for shortcut behavior caused by data artifacts. We demonstrate the benefits of pattern-based CAVs using the Pediatric Bone Age, ISIC2019, and FunnyBirds datasets with VGG, ResNet, and EfficientNet model architectures