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Applying AI in Practice: Key Challenges and Lessons Learned

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Machine Learning and Knowledge Extraction (CD-MAKE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12279))

Abstract

The main challenges along with lessons learned from ongoing research in the application of machine learning systems in practice are discussed, taking into account aspects of theoretical foundations, systems engineering, and human-centered AI postulates. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment.

Special thanks go to A Min Tjoa, former Scientific Director of SCCH, for his encouraging support in bringing together data and software science to tackle the research problems discussed in this paper. The research reported in this paper has been funded by BMK, BMDW, and the Province of Upper Austria in the frame of the COMET Programme managed by FFG.

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Notes

  1. 1.

    https://www.pwc.com/gx/en/services/people-organisation/workforce-of-the-future/workforce-of-the-future-the-competing-forces-shaping-2030-pwc.pdf.

  2. 2.

    https://www.whitehouse.gov/wp-content/uploads/2019/06/National-AI-Research-and-Development-Strategic-Plan-2019-Update-June-2019.pdf.

  3. 3.

    https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.

  4. 4.

    https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/.

  5. 5.

    https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.

  6. 6.

    https://www.iso.org/committee/6794475/x/catalogue/p/0/u/1/w/0/d/0.

  7. 7.

    https://griffin.incubator.apache.org.

  8. 8.

    https://github.com/mobydq/mobydq.

  9. 9.

    Platform supporting an integrated analysis of image and multiOMICs data based on liquid biopsies for tumor diagnostics – https://www.visiomics.at/.

  10. 10.

    Nuclear Segmentation Pipeline code available: https://github.com/SCCH-KVS/NuclearSegmentationPipeline.

  11. 11.

    BioStudies: https://www.ebi.ac.uk/biostudies/studies/S-BSST265.

  12. 12.

    DeepSNP code available: https://github.com/SCCH-KVS/deepsnp.

  13. 13.

    https://www.aloha-h2020.eu/.

  14. 14.

    https://onnx.ai/.

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Authors and Affiliations

  1. Software Competence Center Hagenberg GmbH (SCCH), Hagenberg, Austria

    Lukas Fischer, Lisa Ehrlinger, Verena Geist, Rudolf Ramler, Florian Sobieczky, Werner Zellinger & Bernhard Moser

  2. Johannes Kepler University, Linz, Austria

    Lisa Ehrlinger

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  1. Lukas Fischer
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  2. Lisa Ehrlinger
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Correspondence to Lukas Fischer .

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Editors and Affiliations

  1. Human-Centered AI Lab, Institute for Medical Informatics, Statistics and Doumentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. UAS St. Pölten, St. Pölten, Austria

    Peter Kieseberg

  3. Institute of Software Technology and Interactive Systems, Technical University of Vienna, Vienna, Austria

    A Min Tjoa

  4. SBA Research, Vienna, Austria

    Edgar Weippl

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Fischer, L. et al. (2020). Applying AI in Practice: Key Challenges and Lessons Learned. In: Holzinger, A., Kieseberg, P., Tjoa, A., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2020. Lecture Notes in Computer Science(), vol 12279. Springer, Cham. https://doi.org/10.1007/978-3-030-57321-8_25

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  • DOI: https://doi.org/10.1007/978-3-030-57321-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57320-1

  • Online ISBN: 978-3-030-57321-8

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