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Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking
Authors:
Ralf Raumanns,
Theresa Elstner,
Louis Ferger-Andrews,
Louise M. Carlsen,
Martin Potthast,
Gerard Schouten,
Josien P. W. Pluim,
Veronika Cheplygina
Abstract:
Machine learning courses often use pre-labeled datasets, hiding the subjectivity of human annotation. This creates students with an overly trusting view of AI data and models, undervaluing interpretive diversity. We investigated whether manual data annotation tasks teach students about subjective labeling.
Study Design: An annotation activity was implemented at two universities: Fontys (Netherla…
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Machine learning courses often use pre-labeled datasets, hiding the subjectivity of human annotation. This creates students with an overly trusting view of AI data and models, undervaluing interpretive diversity. We investigated whether manual data annotation tasks teach students about subjective labeling.
Study Design: An annotation activity was implemented at two universities: Fontys (Netherlands) and IT University Copenhagen (Denmark). Students annotated skin lesion images for hair coverage on a 3-point scale. Surveys were collected from 43 participants measuring their understanding of annotation ambiguity, data quality, bias, fairness, implementation barriers, and pedagogical effectiveness.
Key Findings: Self-reported familiarity with course content increased substantially across all concepts. Most students recognised that personal interpretation affects annotations. Students rated the activity as more effective than traditional lectures for understanding bias. Participants were motivated to learn more.
Main Drawbacks: Emotional unease from viewing medical images was the primary issue. Many students still requested clearer guidelines to reduce disagreement, suggesting they hadn't internalised that disagreement from different perspectives is a learning feature, not a bug.
Recommendations for Future Iterations: Ensure sufficient interpretive ambiguity in materials. Reduce repetitive annotation workload. Mitigate emotional unease from sensitive content. Explicitly frame disagreement as a learning opportunity rather than a problem to solve.
Manual data annotations effectively teach students that human judgment shapes model behavior and that disagreement reflects domain complexity, not just noise.
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Submitted 22 July, 2026;
originally announced July 2026.
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Effect of Demographic Bias on Skin Lesion Classification
Authors:
Ralf Raumanns,
Gerard Schouten,
Veronika Cheplygina,
Josien P. W. Pluim
Abstract:
In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, particularly variations in patient sex and age. We use linear programming to generate datasets with controlled demographic characteristics, allowing systematic investigation of bias effects. Three learning strategies are eval…
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In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, particularly variations in patient sex and age. We use linear programming to generate datasets with controlled demographic characteristics, allowing systematic investigation of bias effects. Three learning strategies are evaluated: a single-task model, a reinforcing multi-task model, and an adversarial learning scheme. Our sex-based analysis indicates that sex-specific training datasets optimise model performance. Notably, including male patients in the training data improved performance for the male subgroup, even in female-majority cases. Reinforcing and adversarial learning schemes narrowed or eliminated bias gaps in balanced and female-majority datasets. However, these strategies proved less effective in male-majority settings, where models continued to perform better for males than females. The two learning schemes showed marginal bias reduction compared to the baseline model in predominantly male patient populations. Age-based analysis demonstrates comparable baseline performance across the three model approaches, with performance declining across age categories. Younger groups consistently achieve the highest performance, regardless of training data distribution. Although balanced training yields optimal results for the youngest age category, performance decreases in older categories. We find that sex biases arise mainly from data imbalances, while age biases consistently favour younger groups regardless of distribution. These distinct mechanisms require targeted mitigation strategies. Additionally, cross-dataset validation on two external datasets revealed that domain shifts notably affect performance and patterns of demographic bias.
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Submitted 2 June, 2026;
originally announced June 2026.
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CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity
Authors:
Georgiana Manolache,
Gerard Schouten,
Joaquin Vanschoren
Abstract:
We present CrypticBio, the largest publicly available multimodal dataset of visually confusing species, specifically curated to support the development of AI models in the context of biodiversity applications. Visually confusing or cryptic species are groups of two or more taxa that are nearly indistinguishable based on visual characteristics alone. While much existing work addresses taxonomic ide…
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We present CrypticBio, the largest publicly available multimodal dataset of visually confusing species, specifically curated to support the development of AI models in the context of biodiversity applications. Visually confusing or cryptic species are groups of two or more taxa that are nearly indistinguishable based on visual characteristics alone. While much existing work addresses taxonomic identification in a broad sense, datasets that directly address the morphological confusion of cryptic species are small, manually curated, and target only a single taxon. Thus, the challenge of identifying such subtle differences in a wide range of taxa remains unaddressed. Curated from real-world trends in species misidentification among community annotators of iNaturalist, CrypticBio contains 52K unique cryptic groups spanning 67K species, represented in 166 million images. Rich research-grade image annotations--including scientific, multicultural, and multilingual species terminology, hierarchical taxonomy, spatiotemporal context, and associated cryptic groups--address multimodal AI in biodiversity research. For easy dataset curation, we provide an open-source pipeline CrypticBio-Curate. The multimodal nature of the dataset beyond vision-language arises from the integration of geographical and temporal data as complementary cues to identifying cryptic species. To highlight the importance of the dataset, we benchmark a suite of state-of-the-art foundation models across CrypticBio subsets of common, unseen, endangered, and invasive species, and demonstrate the substantial impact of geographical context on vision-language zero-shot learning for cryptic species. By introducing CrypticBio, we aim to catalyze progress toward real-world-ready biodiversity AI models capable of handling the nuanced challenges of species ambiguity.
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Submitted 16 May, 2025;
originally announced May 2025.
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Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning
Authors:
Ralf Raumanns,
Gerard Schouten,
Josien P. W. Pluim,
Veronika Cheplygina
Abstract:
The influence of bias in datasets on the fairness of model predictions is a topic of ongoing research in various fields. We evaluate the performance of skin lesion classification using ResNet-based CNNs, focusing on patient sex variations in training data and three different learning strategies. We present a linear programming method for generating datasets with varying patient sex and class label…
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The influence of bias in datasets on the fairness of model predictions is a topic of ongoing research in various fields. We evaluate the performance of skin lesion classification using ResNet-based CNNs, focusing on patient sex variations in training data and three different learning strategies. We present a linear programming method for generating datasets with varying patient sex and class labels, taking into account the correlations between these variables. We evaluated the model performance using three different learning strategies: a single-task model, a reinforcing multi-task model, and an adversarial learning scheme. Our observations include: 1) sex-specific training data yields better results, 2) single-task models exhibit sex bias, 3) the reinforcement approach does not remove sex bias, 4) the adversarial model eliminates sex bias in cases involving only female patients, and 5) datasets that include male patients enhance model performance for the male subgroup, even when female patients are the majority. To generalise these findings, in future research, we will examine more demographic attributes, like age, and other possibly confounding factors, such as skin colour and artefacts in the skin lesions. We make all data and models available on GitHub.
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Submitted 9 December, 2024; v1 submitted 24 July, 2024;
originally announced July 2024.
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Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform
Authors:
Petra Heck,
Gerard Schouten
Abstract:
For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machine learning model. A production-ready AI system needs to be trustworthy, i.e. of high quality. But how to determine this in practice? For traditional software, ISO25000 and its predecessors have since long time been used…
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For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machine learning model. A production-ready AI system needs to be trustworthy, i.e. of high quality. But how to determine this in practice? For traditional software, ISO25000 and its predecessors have since long time been used to define and measure quality characteristics. Recently, quality models for AI systems, based on ISO25000, have been introduced. This paper applies one such quality model to a real-life case study: a deep learning platform for monitoring wildflowers. The paper presents three realistic scenarios sketching what it means to respectively use, extend and incrementally improve the deep learning platform for wildflower identification and counting. Next, it is shown how the quality model can be used as a structured dictionary to define quality requirements for data, model and software. Future work remains to extend the quality model with metrics, tools and best practices to aid AI engineering practitioners in implementing trustworthy AI systems.
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Submitted 23 March, 2023;
originally announced March 2023.
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ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
Authors:
Ralf Raumanns,
Gerard Schouten,
Max Joosten,
Josien P. W. Pluim,
Veronika Cheplygina
Abstract:
We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and classic image processing algorithms. In this paper we first analyse the correlat…
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We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and classic image processing algorithms. In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources. Overall we find weak correlations of non-expert annotations with the diagnostic label, and low agreement between different annotation sources. We then study multi-task learning (MTL) with the annotations as additional labels, and show that non-expert annotations can improve (ensembles of) state-of-the-art convolutional neural networks via MTL. We hope that our dataset can be used in further research into multiple annotations and/or MTL. All data and models are available on Github: https://github.com/raumannsr/ENHANCE.
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Submitted 24 December, 2021; v1 submitted 27 July, 2021;
originally announced July 2021.
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Lessons Learned from Educating AI Engineers
Authors:
Petra Heck,
Gerard Schouten
Abstract:
Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer. In this paper we discuss our programme and the lessons learned for industry and rese…
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Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer. In this paper we discuss our programme and the lessons learned for industry and research.
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Submitted 19 March, 2021;
originally announced March 2021.
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Turning Software Engineers into AI Engineers
Authors:
Petra Heck,
Gerard Schouten
Abstract:
In industry as well as education as well as academics we see a growing need for knowledge on how to apply machine learning in software applications. With the educational programme ICT & AI at Fontys UAS we had to find an answer to the question: "How should we educate software engineers to become AI engineers?" This paper describes our educational programme, the open source tools we use, and the li…
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In industry as well as education as well as academics we see a growing need for knowledge on how to apply machine learning in software applications. With the educational programme ICT & AI at Fontys UAS we had to find an answer to the question: "How should we educate software engineers to become AI engineers?" This paper describes our educational programme, the open source tools we use, and the literature it is based on. After three years of experience, we present our lessons learned for both educational institutions and software engineers in practice.
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Submitted 4 January, 2021; v1 submitted 3 November, 2020;
originally announced November 2020.
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Risk of Training Diagnostic Algorithms on Data with Demographic Bias
Authors:
Samaneh Abbasi-Sureshjani,
Ralf Raumanns,
Britt E. J. Michels,
Gerard Schouten,
Veronika Cheplygina
Abstract:
One of the critical challenges in machine learning applications is to have fair predictions. There are numerous recent examples in various domains that convincingly show that algorithms trained with biased datasets can easily lead to erroneous or discriminatory conclusions. This is even more crucial in clinical applications where the predictive algorithms are designed mainly based on a limited or…
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One of the critical challenges in machine learning applications is to have fair predictions. There are numerous recent examples in various domains that convincingly show that algorithms trained with biased datasets can easily lead to erroneous or discriminatory conclusions. This is even more crucial in clinical applications where the predictive algorithms are designed mainly based on a limited or given set of medical images and demographic variables such as age, sex and race are not taken into account. In this work, we conduct a survey of the MICCAI 2018 proceedings to investigate the common practice in medical image analysis applications. Surprisingly, we found that papers focusing on diagnosis rarely describe the demographics of the datasets used, and the diagnosis is purely based on images. In order to highlight the importance of considering the demographics in diagnosis tasks, we used a publicly available dataset of skin lesions. We then demonstrate that a classifier with an overall area under the curve (AUC) of 0.83 has variable performance between 0.76 and 0.91 on subgroups based on age and sex, even though the training set was relatively balanced. Moreover, we show that it is possible to learn unbiased features by explicitly using demographic variables in an adversarial training setup, which leads to balanced scores per subgroups. Finally, we discuss the implications of these results and provide recommendations for further research.
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Submitted 17 June, 2020; v1 submitted 20 May, 2020;
originally announced May 2020.
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Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis
Authors:
Ralf Raumanns,
Elif K Contar,
Gerard Schouten,
Veronika Cheplygina
Abstract:
Machine learning has a recognised need for large amounts of annotated data. Due to the high cost of expert annotations, crowdsourcing, where non-experts are asked to label or outline images, has been proposed as an alternative. Although many promising results are reported, the quality of diagnostic crowdsourced labels is still unclear. We propose to address this by instead asking the crowd about v…
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Machine learning has a recognised need for large amounts of annotated data. Due to the high cost of expert annotations, crowdsourcing, where non-experts are asked to label or outline images, has been proposed as an alternative. Although many promising results are reported, the quality of diagnostic crowdsourced labels is still unclear. We propose to address this by instead asking the crowd about visual features of the images, which can be provided more intuitively, and by using these features in a multi-task learning framework through ensemble strategies. We compare our proposed approach to a baseline model with a set of 2000 skin lesions from the ISIC 2017 challenge dataset. The baseline model only predicts a binary label from the skin lesion image, while our multi-task model also predicts one of the following features: asymmetry of the lesion, border irregularity and color. We show that multi-task models with individual crowdsourced features have limited effect on the model, but when combined in an ensembles, leads to improved generalisation. The area under the receiver operating characteristic curve is 0.794 for the baseline model and 0.811 and 0.808 for multi-task ensembles respectively. Finally, we discuss the findings, identify some limitations and recommend directions for further research. The code of the models is available at https://github.com/raumannsr/hints_crowd.
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Submitted 6 July, 2020; v1 submitted 28 April, 2020;
originally announced April 2020.