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1. Workshop Responsible Data Science - Opening by Will van der Aalst

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To future-proof responsible data science methods, foundational research is needed, and, given the complementarity of TU/e and TiU in JADS, there are great opportunities to collaborate on this theme. This was reflected by the workshop.

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1. Workshop Responsible Data Science - Opening by Will van der Aalst

  1. 1. JADS Workshop on Responsible Data Science (RDS) Wil van der Aalst (DSC/e) Dick den Hertog (DSC/t) Arjan Van Den Born (DSC/d)
  2. 2. Program  9.00-9.05 Opening by Wil van der Aalst  9.05-9.35 Presentation by Viktor Mayer-Schönberger  9.35-9.55 Discussion on Fairness in data science: “Data science without prejudice – How to avoid unfair conclusions even if they are true?” (session leader dr. Maurits Kaptein)  9.55-10.15 Discussion on Accuracy in data science: “Data science without guesswork – How to answer questions with a guaranteed level of accuracy?” (session leader prof.dr. Mykola Pechenizkiy)  10.15-10.35 Discussion on Confidentiality in data science: “Data science that ensures confidentiality – How to answer questions without revealing secrets?” (session leader prof.dr. Ronald Leenes)  10.35-10.55 Discussion on Transparency in data science: “Data science that provides transparency – How to clarify answers such that they become indisputable?” (session leader prof.dr.ir. Jack van Wijk)  10.55-11.00 Closing by Dick den Hertog
  3. 3. ©Wil van der Aalst & TU/e (use only with permission & acknowledgements)©Wil van der Aalst & TU/e (use only with permission & acknowledgements) If data is the new oil on which our society runs, …
  4. 4. ©Wil van der Aalst & TU/e (use only with permission & acknowledgements)©Wil van der Aalst & TU/e (use only with permission & acknowledgements) unfairuseofdata privacyviolations bogusconclusions non-transparent … then we should take care of data-related forms of pollution! spuriouscorrelations
  5. 5. Green data science: separate the “pollution” from the actual purpose
  6. 6. Fairness: Data Science without prejudice: How to avoid unfair conclusions even if they are true?
  7. 7. Fairness: Data Science without prejudice: How to avoid unfair conclusions even if they are true? session leader dr. Maurits Kaptein
  8. 8. Accuracy: Data Science without guesswork: How to answer questions with a guaranteed level of accuracy?
  9. 9. Accuracy: Data Science without guesswork: How to answer questions with a guaranteed level of accuracy? session leader prof.dr. Mykola Pechenizkiy
  10. 10. Confidentiality: Data Science that ensures confidentiality: How to answer questions without revealing secrets?
  11. 11. Confidentiality: Data Science that ensures confidentiality: How to answer questions without revealing secrets? prof.dr. Ronald Leenes
  12. 12. Transparency: Data Science that provides transparency: How to clarify answers such that they become indisputable?
  13. 13. Transparency: Data Science that provides transparency: How to clarify answers such that they become indisputable? prof.dr.ir. Jack van Wijk
  14. 14. www.responsibledatascience.org Join us!
  15. 15. Program  9.00-9.05 Opening by Wil van der Aalst  9.05-9.35 Presentation by Viktor Mayer-Schönberger  9.35-9.55 Discussion on Fairness in data science: “Data science without prejudice – How to avoid unfair conclusions even if they are true?” (session leader dr. Maurits Kaptein)  9.55-10.15 Discussion on Accuracy in data science: “Data science without guesswork – How to answer questions with a guaranteed level of accuracy?” (session leader prof.dr. Mykola Pechenizkiy)  10.15-10.35 Discussion on Confidentiality in data science: “Data science that ensures confidentiality – How to answer questions without revealing secrets?” (session leader prof.dr. Ronald Leenes)  10.35-10.55 Discussion on Transparency in data science: “Data science that provides transparency – How to clarify answers such that they become indisputable?” (session leader prof.dr.ir. Jack van Wijk)  10.55-11.00 Closing by Dick den Hertog
  16. 16. [20] • Professor of Internet Governance and Regulation at the Oxford Internet Institute, University of Oxford. • Previous: Harvard's John F. Kennedy School of Government. • He is the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think(HMH, 2013) and author of Delete: The Virtue of Forgetting in the Digital Age (Princeton, 2009. Viktor Mayer-Schönberger

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