Felix Biessmann

Ahmet-Serdar Karakaya, Thomas Ritter, Felix Biessmann et al.

In cities worldwide, cars cause health and traffic problems whichcould be partly mitigated through an increased modal share of bicycles. Many people, however, avoid cycling due to a lack of perceived safety. For city planners, addressing this is hard as they lack insights intowhere cyclists feel safe and where they do not. To gain such insights,we have in previous work proposed the crowdsourcing platform SimRa,which allows cyclists to record their rides and report near miss incidentsvia a smartphone app. In this paper, we present CycleSense, a combination of signal pro-cessing and Machine Learning techniques, which partially automatesthe detection of near miss incidents, thus making the reporting of nearmiss incidents easier. Using the SimRa data set, we evaluate CycleSenseby comparing it to a baseline method used by SimRa and show that itsignificantly improves incident detection.

Sebastian Jäger, Jessica Greene, Max Jakob et al.

The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommender systems. Thus, ML can potentially support efforts towards more sustainable consumption patterns, for example, by accounting for sustainability aspects in product search or recommendations. However, leveraging ML potential for reaching sustainability goals requires data on sustainability. Unfortunately, no open and publicly available database integrates sustainability information on a product-by-product basis. In this work, we present the GreenDB, which fills this gap. Based on search logs of millions of users, we prioritize which products users care about most. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs to improve sustainability information available for search and recommendation experiences. We present our proof of concept implementation of a scraping system that creates the GreenDB dataset.

Teodor Chiaburu, Felix Biessmann, Frank Hausser

Insects are a crucial part of our ecosystem. Sadly, in the past few decades, their numbers have worryingly decreased. In an attempt to gain a better understanding of this process and monitor the insects populations, Deep Learning may offer viable solutions. However, given the breadth of their taxonomy and the typical hurdles of fine grained analysis, such as high intraclass variability compared to low interclass variability, insect classification remains a challenging task. There are few benchmark datasets, which impedes rapid development of better AI models. The annotation of rare species training data, however, requires expert knowledge. Explainable Artificial Intelligence (XAI) could assist biologists in these annotation tasks, but choosing the optimal XAI method is difficult. Our contribution to these research challenges is threefold: 1) a dataset of thoroughly annotated images of wild bees sampled from the iNaturalist database, 2) a ResNet model trained on the wild bee dataset achieving classification scores comparable to similar state-of-the-art models trained on other fine-grained datasets and 3) an investigation of XAI methods to support biologists in annotation tasks.

Sebastian Jäger, Alexander Flick, Jessica Adriana Sanchez Garcia et al.

The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Machine Learning (ML) can help to foster sustainable consumption patterns by accounting for sustainability aspects in product search or recommendations of modern retail platforms. However, the lack of large high quality publicly available product data with trustworthy sustainability information impedes the development of ML technology that can help to reach our sustainability goals. Here we present GreenDB, a database that collects products from European online shops on a weekly basis. As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs. We present initial results demonstrating that ML models trained with our data can reliably (F1 score 96%) predict the sustainability label of products. These contributions can help to complement existing e-commerce experiences and ultimately encourage users to more sustainable consumption patterns.

Felix Biessmann

Online social media have become an important forum for exchanging political opinions. In response to COVID measures citizens expressed their policy preferences directly on these platforms. Quantifying political preferences in online social media remains challenging: The vast amount of content requires scalable automated extraction of political preferences -- however fine grained political preference extraction is difficult with current machine learning (ML) technology, due to the lack of data sets. Here we present a novel data set of tweets with fine grained political preference annotations. A text classification model trained on this data is used to extract policy preferences in a German Twitter corpus ranging from 2019 to 2022. Our results indicate that in response to the COVID pandemic, expression of political opinions increased. Using a well established taxonomy of policy preferences we analyse fine grained political views and highlight changes in distinct political categories. These analyses suggest that the increase in policy preference expression is dominated by the categories pro-welfare, pro-education and pro-governmental administration efficiency. All training data and code used in this study are made publicly available to encourage other researchers to further improve automated policy preference extraction methods. We hope that our findings contribute to a better understanding of political statements in online social media and to a better assessment of how COVID measures impact political preferences.

Alexander Flick, Sebastian Jäger, Ivana Trajanovska et al.

Extracting structured information from unstructured data is one of the key challenges in modern information retrieval applications, including e-commerce. Here, we demonstrate how recent advances in machine learning, combined with a recently published multilingual data set with standardized fine-grained product category information, enable robust product attribute extraction in challenging transfer learning settings. Our models can reliably predict product attributes across online shops, languages, or both. Furthermore, we show that our models can be used to match product taxonomies between online retailers.

Teodor Chiaburu, Felix Biessmann

Climate change poses increasingly complex challenges to our society. Extreme weather events such as floods, wild fires or droughts are becoming more frequent, spontaneous and difficult to foresee or counteract. In this work we specifically address the problem of sewage water polluting surface water bodies after spilling over from rain tanks as a consequence of heavy rain events. We investigate to what extent state-of-the-art interpretable time series models can help predict such critical water level points, so that the excess can promptly be redistributed across the sewage network. Our results indicate that modern time series models can contribute to better waste water management and prevention of environmental pollution from sewer systems. All the code and experiments can be found in our repository: https://github.com/TeodorChiaburu/RIWWER_TimeSeries.

Johannes Hoster, Sara Al-Sayed, Felix Biessmann et al.

Satellite imagery is regarded as a great opportunity for citizen-based monitoring of activities of interest. Relevant imagery may however not be available at sufficiently high resolution, quality, or cadence -- let alone be uniformly accessible to open-source analysts. This limits an assessment of the true long-term potential of citizen-based monitoring of nuclear activities using publicly available satellite imagery. In this article, we demonstrate how modern game engines combined with advanced machine-learning techniques can be used to generate synthetic imagery of sites of interest with the ability to choose relevant parameters upon request; these include time of day, cloud cover, season, or level of activity onsite. At the same time, resolution and off-nadir angle can be adjusted to simulate different characteristics of the satellite. While there are several possible use-cases for synthetic imagery, here we focus on its usefulness to support tabletop exercises in which simple monitoring scenarios can be examined to better understand verification capabilities enabled by new satellite constellations and very short revisit times.

Tianheng Ling, Vipin Singh, Chao Qian et al.

Extreme weather events, intensified by climate change, increasingly challenge aging combined sewer systems, raising the risk of untreated wastewater overflow. Accurate forecasting of sewer overflow basin filling levels can provide actionable insights for early intervention, helping mitigating uncontrolled discharge. In recent years, AI-based forecasting methods have offered scalable alternatives to traditional physics-based models, but their reliance on cloud computing limits their reliability during communication outages. To address this, we propose an end-to-end forecasting framework that enables energy-efficient inference directly on edge devices. Our solution integrates lightweight Transformer and Long Short-Term Memory (LSTM) models, compressed via integer-only quantization for efficient on-device execution. Moreover, an automated hardware-aware deployment pipeline is used to search for optimal model configurations by jointly minimizing prediction error and energy consumption on an AMD Spartan-7 XC7S15 FPGA. Evaluated on real-world sewer data, the selected 8-bit Transformer model, trained on 24 hours of historical measurements, achieves high accuracy (MSE 0.0376) at an energy cost of 0.370 mJ per inference. In contrast, the optimal 8-bit LSTM model requires significantly less energy (0.009 mJ, over 40x lower) but yields 14.89% worse accuracy (MSE 0.0432) and much longer training time. This trade-off highlights the need to align model selection with deployment priorities, favoring LSTM for ultra-low energy consumption or Transformer for higher predictive accuracy. In general, our work enables local, energy-efficient forecasting, contributing to more resilient combined sewer systems. All code can be found in the GitHub Repository (https://github.com/tianheng-ling/EdgeOverflowForecast).

Vipin Singh, Tianheng Ling, Teodor Chiaburu et al.

Climate change poses complex challenges, with extreme weather events becoming increasingly frequent and difficult to model. Examples include the dynamics of Combined Sewer Systems (CSS). Overburdened CSS during heavy rainfall will overflow untreated wastewater into surface water bodies. Classical approaches to modeling the impact of extreme rainfall events rely on physical simulations, which are particularly challenging to create for large urban infrastructures. Deep Learning (DL) models offer a cost-effective alternative for modeling the complex dynamics of sewer systems. In this study, we present a comprehensive empirical evaluation of several state-of-the-art DL time series models for predicting sewer system dynamics in a large urban infrastructure, utilizing three years of measurement data. We especially investigate the potential of DL models to maintain predictive precision during network outages by comparing global models, which have access to all variables within the sewer system, and local models, which are limited to data from a restricted set of local sensors. Our findings demonstrate that DL models can accurately predict the dynamics of sewer system load, even under network outage conditions. These results suggest that DL models can effectively aid in balancing the load redistribution in CSS, thereby enhancing the sustainability and resilience of urban infrastructures.

Pallavi Mitra, Felix Biessmann

Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to automate the task of hyperparameter optimization (HPO) in machine learning (ML) models with the primary objective of optimizing predictive performance on held-out data. In recent years, however, with ever-growing model sizes, the energy cost associated with model training has become an important factor for ML applications. Here we evaluate Constrained Bayesian Optimization (CBO) with the primary objective of minimizing energy consumption and subject to the constraint that the generalization performance is above some threshold. We evaluate our approach on regression and classification tasks and demonstrate that CBO achieves lower energy consumption without compromising the predictive performance of ML models.

Tuong Vy Nguyen, Johannes Hoster, Alexander Glaser et al.

Generative deep learning architectures can produce realistic, high-resolution fake imagery -- with potentially drastic societal implications. A key question in this context is: How easy is it to generate realistic imagery, in particular for niche domains. The iterative process required to achieve specific image content is difficult to automate and control. Especially for rare classes, it remains difficult to assess fidelity, meaning whether generative approaches produce realistic imagery and alignment, meaning how (well) the generation can be guided by human input. In this work, we present a large-scale empirical evaluation of generative architectures which we fine-tuned to generate synthetic satellite imagery. We focus on nuclear power plants as an example of a rare object category - as there are only around 400 facilities worldwide, this restriction is exemplary for many other scenarios in which training and test data is limited by the restricted number of occurrences of real-world examples. We generate synthetic imagery by conditioning on two kinds of modalities, textual input and image input obtained from a game engine that allows for detailed specification of the building layout. The generated images are assessed by commonly used metrics for automatic evaluation and then compared with human judgement from our conducted user studies to assess their trustworthiness. Our results demonstrate that even for rare objects, generation of authentic synthetic satellite imagery with textual or detailed building layouts is feasible. In line with previous work, we find that automated metrics are often not aligned with human perception -- in fact, we find strong negative correlations between commonly used image quality metrics and human ratings.

Vipin Singh, Tianheng Ling, Peter Ghaly et al.

Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows (CSO) with significant environmental and public health impacts. Forecasting the filling dynamics of overflow basins is critical for anticipating capacity exceedance and enabling timely preventive actions for CSO. We present a web-based demonstrator (https://riwwer.demo.calgo-lab.de) that integrates Deep Learning forecasting methods in both cloud and edge settings into an interactive monitoring dashboard for overflow monitoring, resilient to network outages. A video showcase is available online (https://cloud.bht-berlin.de/index.php/s/b9xt4T3SdiLBiFZ).

Vipin Singh, Tianheng Ling, Teodor Chiaburu et al.

Climate change increases the frequency of extreme rainfall, placing a significant strain on urban infrastructures, especially Combined Sewer Systems (CSS). Overflows from overburdened CSS release untreated wastewater into surface waters, posing environmental and public health risks. Although traditional physics-based models are effective, they are costly to maintain and difficult to adapt to evolving system dynamics. Machine Learning (ML) approaches offer cost-efficient alternatives with greater adaptability. To systematically assess the potential of ML for modeling urban infrastructure systems, we propose a protocol for evaluating Neural Network architectures for CSS time series forecasting with respect to predictive performance, model complexity, and robustness to perturbations. In addition, we assess model performance on peak events and critical fluctuations, as these are the key regimes for urban wastewater management. To investigate the feasibility of lightweight models suitable for IoT deployment, we compare global models, which have access to all information, with local models, which rely solely on nearby sensor readings. Additionally, to explore the security risks posed by network outages or adversarial attacks on urban infrastructure, we introduce error models that assess the resilience of models. Our results demonstrate that while global models achieve higher predictive performance, local models provide sufficient resilience in decentralized scenarios, ensuring robust modeling of urban infrastructure. Furthermore, models with longer native forecast horizons exhibit greater robustness to data perturbations. These findings contribute to the development of interpretable and reliable ML solutions for sustainable urban wastewater management. The implementation is available in our GitHub repository.

Philipp Jung, Nicholas Chandler, Sebastian Jäger et al.

Data quality monitoring is a core challenge in modern information processing systems. While many approaches to detect data errors or shifts have been proposed, few studies investigate the mechanisms governing error generation. We argue that knowing how errors were generated can be key to tracing and fixing them. In this study, we build on existing work in the statistics literature on missing values and propose MechDetect, a simple algorithm to investigate error generation mechanisms. Given a tabular data set and a corresponding error mask, the algorithm estimates whether or not the errors depend on the data using machine learning models. Our work extends established approaches to detect mechanisms underlying missing values and can be readily applied to other error types, provided that an error mask is available. We demonstrate the effectiveness of MechDetect in experiments on established benchmark datasets.

Mario Koddenbrock, Christoph Lange, Robin Legner et al.

Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive molecular analysis, is one such field where progress is limited by fragmented datasets, inconsistent evaluation, and models that fail to capture the structure of spectral data. We introduce RamanBench, the first large-scale, fully reproducible benchmark for ML on Raman spectroscopy, consisting of streamlined data access, evaluation protocols and code, as well as a live leaderboard. It unifies 74 datasets (including 16 first released with this benchmark) across four domains, comprising 325,668 spectra and spanning classification and regression tasks under diverse experimental conditions. We benchmark 28 models under a standardized protocol, including classical methods (e.g., PLS), Raman-specific (e.g., RamanNet), Tabular Foundation Model (TFM) (e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFM consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no method generalizes across datasets, revealing a fundamental gap. Therefore, we invite the community to contribute new approaches to our living benchmark, with the potential to accelerate advances in critical applications such as medical diagnostics, biological research, and materials science.

Tuong Vy Nguyen, Alexander Glaser, Felix Biessmann

Novel deep-learning (DL) architectures have reached a level where they can generate digital media, including photorealistic images, that are difficult to distinguish from real data. These technologies have already been used to generate training data for Machine Learning (ML) models, and large text-to-image models like DALL-E 2, Imagen, and Stable Diffusion are achieving remarkable results in realistic high-resolution image generation. Given these developments, issues of data authentication in monitoring and verification deserve a careful and systematic analysis: How realistic are synthetic images? How easily can they be generated? How useful are they for ML researchers, and what is their potential for Open Science? In this work, we use novel DL models to explore how synthetic satellite images can be created using conditioning mechanisms. We investigate the challenges of synthetic satellite image generation and evaluate the results based on authenticity and state-of-the-art metrics. Furthermore, we investigate how synthetic data can alleviate the lack of data in the context of ML methods for remote-sensing. Finally we discuss implications of synthetic satellite imagery in the context of monitoring and verification.

Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini et al.

The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of trust by the users. These issues have led to the adoption of methods like SHAP and Integrated Gradients to explain classification decisions by assigning importance scores to input tokens. However, prior work, using different randomization tests, has shown that interpretations generated by these methods may not be robust. For instance, models making the same predictions on the test set may still lead to different feature importance rankings. In order to address the lack of robustness of token-based interpretability, we explore explanations at higher semantic levels like sentences. We use computational metrics and human subject studies to compare the quality of sentence-based interpretations against token-based ones. Our experiments show that higher-level feature attributions offer several advantages: 1) they are more robust as measured by the randomization tests, 2) they lead to lower variability when using approximation-based methods like SHAP, and 3) they are more intelligible to humans in situations where the linguistic coherence resides at a higher granularity level. Based on these findings, we show that token-based interpretability, while being a convenient first choice given the input interfaces of the ML models, is not the most effective one in all situations.

Felix Biessmann, Dionysius Refiano

The field explainable artificial intelligence (XAI) has brought about an arsenal of methods to render Machine Learning (ML) predictions more interpretable. But how useful explanations provided by transparent ML methods are for humans remains difficult to assess. Here we investigate the quality of interpretable computer vision algorithms using techniques from psychophysics. In crowdsourced annotation tasks we study the impact of different interpretability approaches on annotation accuracy and task time. We compare these quality metrics with classical XAI, automated quality metrics. Our results demonstrate that psychophysical experiments allow for robust quality assessment of transparency in machine learning. Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans. These findings highlight the potential of methods from classical psychophysics for modern machine learning applications. We hope that our results provide convincing arguments for evaluating interpretability in its natural habitat, human-ML interaction, if the goal is to obtain an authentic assessment of interpretability.

Felix Biessmann, Viktor Treu

A central goal of explainable artificial intelligence (XAI) is to improve the trust relationship in human-AI interaction. One assumption underlying research in transparent AI systems is that explanations help to better assess predictions of machine learning (ML) models, for instance by enabling humans to identify wrong predictions more efficiently. Recent empirical evidence however shows that explanations can have the opposite effect: When presenting explanations of ML predictions humans often tend to trust ML predictions even when these are wrong. Experimental evidence suggests that this effect can be attributed to how intuitive, or human, an AI or explanation appears. This effect challenges the very goal of XAI and implies that responsible usage of transparent AI methods has to consider the ability of humans to distinguish machine generated from human explanations. Here we propose a quantitative metric for XAI methods based on Turing's imitation game, a Turing Test for Transparency. A human interrogator is asked to judge whether an explanation was generated by a human or by an XAI method. Explanations of XAI methods that can not be detected by humans above chance performance in this binary classification task are passing the test. Detecting such explanations is a requirement for assessing and calibrating the trust relationship in human-AI interaction. We present experimental results on a crowd-sourced text classification task demonstrating that even for basic ML models and XAI approaches most participants were not able to differentiate human from machine generated explanations. We discuss ethical and practical implications of our results for applications of transparent ML.

Felix Biessmann, Dionysius Irza Refiano

The field of transparent Machine Learning (ML) has contributed many novel methods aiming at better interpretability for computer vision and ML models in general. But how useful the explanations provided by transparent ML methods are for humans remains difficult to assess. Most studies evaluate interpretability in qualitative comparisons, they use experimental paradigms that do not allow for direct comparisons amongst methods or they report only offline experiments with no humans in the loop. While there are clear advantages of evaluations with no humans in the loop, such as scalability, reproducibility and less algorithmic bias than with humans in the loop, these metrics are limited in their usefulness if we do not understand how they relate to other metrics that take human cognition into account. Here we investigate the quality of interpretable computer vision algorithms using techniques from psychophysics. In crowdsourced annotation tasks we study the impact of different interpretability approaches on annotation accuracy and task time. In order to relate these findings to quality measures for interpretability without humans in the loop we compare quality metrics with and without humans in the loop. Our results demonstrate that psychophysical experiments allow for robust quality assessment of transparency in machine learning. Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans. These findings highlight the potential of methods from classical psychophysics for modern machine learning applications. We hope that our results provide convincing arguments for evaluating interpretability in its natural habitat, human-ML interaction, if the goal is to obtain an authentic assessment of interpretability.

Philipp Schmidt, Felix Biessmann

Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this context is that both the quality of interpretability methods as well as trust in ML predictions are difficult to measure. Yet evaluations, comparisons and improvements of trust and interpretability require quantifiable measures. Here we propose a quantitative measure for the quality of interpretability methods. Based on that we derive a quantitative measure of trust in ML decisions. Building on previous work we propose to measure intuitive understanding of algorithmic decisions using the information transfer rate at which humans replicate ML model predictions. We provide empirical evidence from crowdsourcing experiments that the proposed metric robustly differentiates interpretability methods. The proposed metric also demonstrates the value of interpretability for ML assisted human decision making: in our experiments providing explanations more than doubled productivity in annotation tasks. However unbiased human judgement is critical for doctors, judges, policy makers and others. Here we derive a trust metric that identifies when human decisions are overly biased towards ML predictions. Our results complement existing qualitative work on trust and interpretability by quantifiable measures that can serve as objectives for further improving methods in this field of research.

Felix Biessmann

Every day media generate large amounts of text. An unbiased view on media reports requires an understanding of the political bias of media content. Assistive technology for estimating the political bias of texts can be helpful in this context. This study proposes a simple statistical learning approach to predict political bias from text. Standard text features extracted from speeches and manifestos of political parties are used to predict political bias in terms of political party affiliation and in terms of political views. Results indicate that political bias can be predicted with above chance accuracy. Mistakes of the model can be interpreted with respect to changes of policies of political actors. Two approaches are presented to make the results more interpretable: a) discriminative text features are related to the political orientation of a party and b) sentiment features of texts are correlated with a measure of political power. Political power appears to be strongly correlated with positive sentiment of a text. To highlight some potential use cases a web application shows how the model can be used for texts for which the political bias is not clear such as news articles.

Felix Biessmann, Jens-Michalis Papaioannou, Mikio Braun et al.

Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem in the context of web data mining is to detect those web sources that are first to publish information which will give rise to a trend. We present a simple and efficient method for finding trends dominating a pool of web sources and identifying those web sources that publish the information relevant to a trend before others. We validate our approach on real data collected from influential technology news feeds.