FAIR for AI: An interdisciplinary and international community building perspective

Huerta, E. A.; Blaiszik, Ben; Brinson, L. Catherine; Bouchard, Kristofer E.; Díaz, D.; Doglioni, C.; Duarte, J.; Emani, Murali; Foster, Ian; Fox, Geoffrey; Harris, P.; Heinrich, L.; Jha, Shantenu; Kindratenko, Volodymyr; Kirkpatrick, Christine; Lassila-Perini, K.; Madduri, Ravi; Neubauer, M. S.; Psomopoulos, Fotis; Roy, A.; Rübel, Oliver; Zhao, Z. G.

doi:10.1038/s41597-023-02298-6

Cited by 27 publications

(4 citation statements)

References 63 publications

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“…This algorithm was developed by complying with methods that will allow its communication with multiple devices. This, in line with the FAIR principles, which were issued in 2016 to serve as a guide for data management and stewardship [24]. These principles indicate that data should be made findable, accessible, interoperable and reusable.…”

Section: Discussionsupporting

confidence: 59%

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors—A Multicentric Study

Saraiva,

Spindler,

Manzione

et al. 2024

Cancers

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High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.

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Section: Discussionsupporting

confidence: 59%

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors—A Multicentric Study

Saraiva,

Spindler,

Manzione

et al. 2024

Cancers

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“…These studies represent a step toward a FAIR ecosystem of data and AI models to enable and streamline automated AI-driven scientific discovery across disciplines [95]. Future work in this area will need to address many outstanding issues, such as providing documentation in a machine-readable way, as well as the development of standardized APIs for federating searching, accessing, and interoperating AI models hosted on different platforms, such as GitHub, DLHub, AI Model Share, and Hugging Face.…”

Section: Discussionmentioning

confidence: 99%

FAIR AI models in high energy physics

Duarte,

Li,

Roy

et al. 2023

Mach. Learn.: Sci. Technol.

Self Cite

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The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

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“…We endeavoured to make this dataset FAIR: i) findable through using a consistent and unique sample ID for each formulation; ii) accessible by sharing the dataset on a public repository; iii) interoperable through the use of a common and system-agnostic data format (JSON), and iv) reusable with an open licence to use this dataset and thereby follow the FAIR community guidelines for data management 46 , 47 .…”

Section: Data Recordsmentioning

confidence: 99%

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Chitre,

Querimit,

Rihm

et al. 2024

Sci Data

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Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.

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FAIR for AI: An interdisciplinary and international community building perspective

Cited by 27 publications

References 63 publications

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors—A Multicentric Study

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors—A Multicentric Study

FAIR AI models in high energy physics

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

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