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Data Sensitivity and Domain Specificity in Reuse of Machine Learning Applications

Author

Listed:
  • Corinna Rutschi

    (University of Bern)

  • Nicholas Berente

    (Mendoza College of Business Notre Dame)

  • Frederick Nwanganga

    (Mendoza College of Business Notre Dame)

Abstract

Data sensitivity and domain specificity challenges arise in reuse of machine learning applications. We identify four types of machine learning applications based on different reuse strategies: generic, distinctive, selective, and exclusive. We conclude with lessons for developing and deploying machine learning applications.

Suggested Citation

  • Corinna Rutschi & Nicholas Berente & Frederick Nwanganga, 2024. "Data Sensitivity and Domain Specificity in Reuse of Machine Learning Applications," Information Systems Frontiers, Springer, vol. 26(2), pages 633-640, April.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:2:d:10.1007_s10796-023-10388-4
    DOI: 10.1007/s10796-023-10388-4
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    References listed on IDEAS

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    1. Karthik Panchanathan & Robert Boyd, 2004. "Indirect reciprocity can stabilize cooperation without the second-order free rider problem," Nature, Nature, vol. 432(7016), pages 499-502, November.
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