hypocrisy, scam ... at work everyday
not even counting, and much more often than "fake news"
It's damn easy to get wrong using utterly right figures (*)
Lasciate ogne speranza, voi ch'intrate Dante's Inferno, 1313
WashingtonPost: Meta .vs. Facebook
from "fake-news" for everyone to "fake-faces" for each one
GeckoBoard: about statistical-fallacies
sort of 1950's "How to Lie with Statistics" revisit
The Ringer (Mar.26 2021)
About social media alternatives (?)
DirtyDataScience project (2018-2021)
Statistical learning on non-curated data
TowardsDataScience: about M-L (2020)
Machine-Learning going wrong
Guardian: about data-Moguls (Jul. 2020)
US Congress .vs. GAFAs to answer for their misdeeds
Anodot: about data quality (Jun. 2020)
The Price You Pay for Poor Data Quality
Dataversity: about data quality (Apr. 2020)
How Much Data Quality is Good Enough?
Wire: wrong with right (Jun. 2019)
Right Data, Wrong Conclusions: a controversy
Government Technology (Mar. 2017)
When Big Data Gets It Wrong
Science Mag special issue (Feb. 2017)
Why turning data into policy is harder than it sounds
Science Mag news (Feb. 2017)
Clash at NOAA about quality assurance protocols
Science Mag: public data quality (Jan. 2016)
Improving the quality of publicly archived data
Hey, data ! get bigger, don't get
more buggy
more buggy