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Governance and Regulations Implications on Machine Learning (Brief Announcement)

  • Sima NadlerEmail author
  • Orna RazEmail author
  • Marcel ZalmanoviciEmail author
Conference paper
  • 541 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11527)

Abstract

Machine learning systems’ efficacy are highly dependent on their training data and the data they receive during production. However, current data governance policies and privacy laws dictate when and how personal and other sensitive data may be used. This affects the amount and quality of personal data included for training, potentially introducing bias and other inaccuracies into the model. Today’s mechanisms do not provide (a) a way for the model developer to know about this nor, (b) to alleviate the bias. This paper addresses both of these challenges.

Keywords

Data governance Implications Privacy Machine learning 

References

  1. 1.
    GIZMODO: Amazon’s secret ai hiring tool reportedly ‘penalized’ resumes with the word ‘women’s’. One of many reports on the topic (2018). gizmodo.com/amazons-secret-ai-hiring-tool-reportedly-penalized-resu-1829649346
  2. 2.
    Sima Nadler, O.R., Zalmanovici, M.: Governance and regulations implications on machine learning. http://www.research.ibm.com/haifa/dept/vst/papers/Data_Governance.pdf

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IBM ResearchHaifaIsrael

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