Abstract

Much of the research that our community publishes is based on data. However, an open question remains: Are the results of data science trustworthy, and how can we increase our trust in data science? Accomplishing this goal is difficult, as we must trust the inputs, systems, and results of data science. This panel will discuss the current state of trustworthy data science, and explore possible technical, legal, and cultural solutions that can increase our trust in the input, systems, and results of data science.

Original languageEnglish (US)
Title of host publicationCODASPY 2017 - Proceedings of the 7th ACM Conference on Data and Application Security and Privacy
PublisherAssociation for Computing Machinery, Inc
Pages217
Number of pages1
ISBN (Electronic)9781450345231
DOIs
StatePublished - Mar 22 2017
Event7th ACM Conference on Data and Application Security and Privacy, CODASPY 2017 - Scottsdale, United States
Duration: Mar 22 2017Mar 24 2017

Other

Other7th ACM Conference on Data and Application Security and Privacy, CODASPY 2017
CountryUnited States
CityScottsdale
Period3/22/173/24/17

Keywords

  • Data science
  • Panel
  • Trust

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Software

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  • Cite this

    Doupe, A. (2017). Panel: Trustworthy data science. In CODASPY 2017 - Proceedings of the 7th ACM Conference on Data and Application Security and Privacy (pp. 217). Association for Computing Machinery, Inc. https://doi.org/10.1145/3029806.3044199