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 language | English (US) |
|---|---|
| Title of host publication | CODASPY 2017 - Proceedings of the 7th ACM Conference on Data and Application Security and Privacy |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 217 |
| Number of pages | 1 |
| ISBN (Electronic) | 9781450345231 |
| DOIs | |
| State | Published - Mar 22 2017 |
| Event | 7th ACM Conference on Data and Application Security and Privacy, CODASPY 2017 - Scottsdale, United States Duration: Mar 22 2017 → Mar 24 2017 |
Other
| Other | 7th ACM Conference on Data and Application Security and Privacy, CODASPY 2017 |
|---|---|
| Country | United States |
| City | Scottsdale |
| Period | 3/22/17 → 3/24/17 |
Keywords
- Data science
- Panel
- Trust
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
- Software
Cite this
Panel : Trustworthy data science. / Doupe, Adam.
CODASPY 2017 - Proceedings of the 7th ACM Conference on Data and Application Security and Privacy. Association for Computing Machinery, Inc, 2017. p. 217.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Panel
T2 - Trustworthy data science
AU - Doupe, Adam
PY - 2017/3/22
Y1 - 2017/3/22
N2 - 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.
AB - 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.
KW - Data science
KW - Panel
KW - Trust
UR - http://www.scopus.com/inward/record.url?scp=85018484944&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018484944&partnerID=8YFLogxK
U2 - 10.1145/3029806.3044199
DO - 10.1145/3029806.3044199
M3 - Conference contribution
AN - SCOPUS:85018484944
SP - 217
BT - CODASPY 2017 - Proceedings of the 7th ACM Conference on Data and Application Security and Privacy
PB - Association for Computing Machinery, Inc
ER -