#KDD2019 Plenary Keynote Panel: Why, What & How we Democratize Data Science
Democratization data science
###democratization of Data science ~ Anil Jain ~
- Disperse data science expertise
- Science: Easily accessible
- Data access: Privacy issues, ownership, user consent, data integrity, function creep.
do package for everyone to be able to use it (data science)
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GAFA is monetizing the data
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Personally identifiable information
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information that can be used to trace an individual's identity
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GDPR also covers physical, physiological identity, location, online identifier
Privacy of anonymized data -
who can trust with data?
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Privacy vs Security
##what is democratizing data science? ~Tina Eliasi-Rad
- democratizing data science is the notion that anyone, with little to no expertise, can do data science if provided ample data and user-friendly analytic tools.
data scientist cannot be automated.
pertient questions
- top-down
- experts provide tools
- No experts plug-and-chug without having the requisite skills and/or expertise.
- Bottom-up
- you educate the nonexperts so they develop some of the skills and expertise that data scientists provide.
every person can benefit from data by using data science ~ Dr. Yu Zheng ~
- Data sharing
- copyright of data
- protecting the privacy
- advanced machine learning models
- domain knowledge + data science
- solve domain problem simply and directly
- ecosystem
- No one can solve problems alone
- based on the same foundation
- everyone can contribute to and benefit from it
~Eric Sears~
- Address the diversity crisis & transform power imbalances
- from ethics to justice accountability
- public interest data science
memos (panel discussion)
- put dataset on the platform to be available, (and models)
- for especially student to use it,
- we are unsure that model quality of large scale data, that has experimented on small data